Note: Descriptions are shown in the official language in which they were submitted.
Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
Patent Application of
Hamid Hatami-Hanza
For
TITLE: METHODS AND SYSTEMS FOR INVESTIGATION OF
COMPOSITIONS OF ONTOLOGICAL SUBJECTS AND INTELLIGENT
SYSTEMS THEREFROM
FIELD OF INVENTION
This invention generally relates to information processing, ontological
subject processing,
knowledge processing and discovery, computational genomics, knowledge
retrieval, artificial
intelligence, signal processing, information theory, natural language
processing and the applications.
BACKGROUND OF THE INVENTION
In these day and age that data is generated at an unprecedented rate it is
very hard for a human
operator to analyze large bodies of data in order to extract the real
information, the knowledge
therein, spot a novelty, and using them to further advance the state of
knowledge or discovery of a
real knowledge about a subject matter.
For example for any topic or subject there are vast amount of textual, or
convertible to textual
characters, repositories such as collection of research papers in any
particular topic or subject,
images, news feeds, interviews, talks, video collections, corporate databases,
surveillance pictures
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Patent Application of Hamid Hatanni-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
and videos, and the like. Gaining any benefit from such unstructured
collections of information
needs lots of expertise, time, and many years of training just even to
separate the facts and extract
value out of these immense amounts of data. Not every piece of data is worthy
of attention and
investigation or investment of expensive times of experts and professionals or
data processing
resources.
Moreover, there is no guarantee that a human investigator or researcher can
accurately analyze
the vast collection of documents, data, and information. The results of the
investigations are usually
biased by the individual's knowledge, experiences, and background. The
complexities of relations
in the bodies of data limit the throughput of knowledge-based professionals
and the speed at which
credible knowledge can be produced. The desired speed or rate of knowledge
discovery apparently
is much higher than the present rate of knowledge discovery and production.
SUMMARY OF THE INVENTION
There is a need to enhance the art of knowledge discovery and investigation
methods in terms
of accuracy, effectiveness on unknown compositions, thoroughness, speed, and
throughput.
Additionally, in some instances, there could be compositions such as, an alien
language
composition, a body of knowledge unfamiliar to an individual investigator, a
corporate database, a
computer code program, a collection of reports, genetic code strings and the
like that we do not have
any prior information about the meaning and implications of these compositions
and the parts
therein. Investigating such compositions is of immense interest and value.
It is also very desirable to enable a data processing system, such as a
computer system comprise of data
processing or computing devices/units, data storage units/devices, and/or
environmental data acquisitions
units/devices, and/or data communication units/devices, and/or input/output
units/devices, and/or limbs, to learn
as much information and gain knowledge/data by processing compositions of data
of various forms and/or
become able to produce new knowledge and useful data or compositions of data
and/or autonomous decision
making according to some codes of conducts. Such an enabled machine would be
of an immense assistance to
the development of human civilization much further and much faster leading to
abundance, economic prosperity,
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Patent Application of Hannid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
biological and mental health, and well-being of society in general.
Accordingly, the present invention discloses a systematic, computer
implementable, process
efficient and scalable method/s of investigation of all types of compositions
of ontological subjects
such as textual, data files, networks and graphs, genetic codes, any types of
string, and the likes.
The given methods, algorithms, and services are accompanied with theoretical
modeling and
mathematical formulations which, once implemented, results in robust and
fundamental algorithms
and processes for investigating various aspects of a composition and for
numerous applications.
According to the teachings of the present invention any compositions of
ontological subjects
is viewed as an unknown system or system of knowledge that the purpose of the
investigation is to
obtain as much worthy information and knowledge about such an unknown system.
The present invention therefore investigate the "compositions of ontological
subjects" or a
"body of knowledge" or a "system of knowledge" (as are called from time to
time in this disclosure)
by providing the investigation methods for identifying the most significant
constituent ontological
subjects for a given body of knowledge or the given compositions in respect to
one or more
significance aspect/s. The significance aspects generally include the
"intrinsic significance aspects"
and/or "associational/relational significance aspects".
In the general aspect of this invention, conceptual "measures of
significances" are disclosed
along with their rational and justifications. These conceptual "measures of
significances" further are
accompanied with systematic methods of calculation and quantifications of
their values in order to
provide the instrumental tools in implementations/utilization of the disclosed
method/s of the
investigation of compositions of ontological subjects. These measures are, for
example, called
"value significance measures" (VSM/s in short), "association strength
measures" (or ASM for short),
"novelty value significance measures" (or NVSM for short), and/or
"relational/associational" type
measures, and various combinations of them (referred herein as XY VSM in
general form) that are
used to find and spot the "aspectual significant" parts or partitions of the
composition for further
investigation and/or further processing and/or presentation to a client.
According to one general embodiment of the disclosed method/s of the present
invention, a
composition of ontological subjects or a body of knowledge is break down to
it's constituent
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
ontological subjects which are grouped in different set which each set labeled
with different orders,
from which one or more array of data, respective of the information of the
participations of the
constituent ontological subjects of different orders into each other, are
formed. The data therefore
is used to evaluate various significance values of the constituent ontological
subjects of the different
order according to the disclosed measures of various aspects of significance.
Accordingly, in one aspect of the present invention, measure/s are given for
valuation of "value
significances" of the ontological subjects of the composition. These values
are intrinsic values of
the ontological subjects of the composition based on their significance role
which is calculated from
the participations pattern's of the ontological subjects of the composition
with each other.
In another aspect various measures of "association strength" are given from
which the relations
of ontological subjects of the composition can be revealed. Algorithms and
formulations and
calculation methods are given to evaluate such "association strength"
according to various
exemplary association aspects.
According to another aspect of the present invention measures are given for
evaluating the
"relational association strengths" of the ontological subjects of different
orders to each other or to
one or more target ontological subject.
According to another aspect of the present invention measures are given for
evaluating the
"relational value significances" of the ontological subjects of different
orders to each other or to one
or more target ontological subject.
According to another aspect of the invention, various types of measures are
given to evaluate
the "novelty value significances" of the ontological subjects of the
composition or the body of
knowledge. Method/s are, therefore, given for efficient calculations and
processing and presentation
of the results.
Accordingly, in yet another aspect of the invention, various measure of the
"relational novelty
value significances" are given for evaluating one type of the general "novelty
value significances"
in relation to one or more target ontological subjects of the composition or
the body of knowledge.
According to yet another aspect of the invention various measure of the
"associational novelty
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Patent Application of Hamid Hatanni-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
value significances" are given for evaluating another type of the general
"novelty value
significance" involving the association of one or more target ontological
subjects of the composition
or the body of knowledge.
According to yet another aspect of the invention various measure of the
"intrinsic novelty value
significances" are given for evaluating yet another type of "novel value
significance" which is an
intrinsic novelty value of one or more of ontological subjects of the
composition or the body of
knowledge.
According to another aspect of the invention, the values are assigned to a
predetermined
list of ontological subjects (e.g. one or more of the special words that
usually are used to express
a particular attribute such as a novelty or a reasoning or concluding remarks,
such as 'therefore,
consequently, in spite of,... however, but,... etc.). These are called
"special significance
conveyers" to pre-selectedly amplify or dampen the significances of such
special OS s of a
composition in eth final output or result.
Furthermore, specific examples and general forms and methods are given as how
to synthesize
and/or shape a desired from of a "value significance measure" and how to build
and calculate the
respective filter for that "value significance measure" by combining one or
more of the VSM vectors
of one or more type or number of the XY-VSM.
These various "XY-value significance measures" then can be employed in many
applications
for which at least one "aspectual significance measure" is of interest and
importance. Depends on
the desired application one can use the applicable and desirable embodiments
for the intended
application such as web page ranking, document clustering, single and multi-
document
summarization/distillation, question answering, graphical representation of
the compositions,
context extraction and representation, knowledge discovery, novelty detection,
composing new
compositions, engineering new compositions, composition comparison,
approximate reasoning,
artificial intelligence, robotic, robotics vision, human/computer interaction,
computer conversation,
as well as other areas of science and technology such as genetic analysis and
synthesize, signal
processing, economics, marketing, customer care, and the like.
Along the disclosure, methods, formulations, and algorithms are given for
efficient and
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
versatile computer implementable evaluation of the various "value significance
measures" of
ontological subject of different orders used in a system of knowledge. In
essence, using the
participation information of a set of lower order OSs into a set of the same
or higher order OSs, the
present invention provide a unified method and process of investigating the
compositions of
ontological subjects, modeling an unknown system, and obtaining as much
worthwhile information
and knowledge as possible about the system or the composition or the body of
knowledge. The
"aspectual investigation's goals" can be wide-open, however, in light of the
teachings of the present
invention becomes a straightforward, implementable, and practical possibility.
Accordingly, in another aspect of the invention, a number of exemplary
applications are
described and presented with the illustrating block diagrams of the method and
algorithm along
with the associated systems for performing such applications. These
applications and systems are
presented to exemplify the way that the present invention's methods of
investigations might be
employed to perform one or more of the desired processes to get the respective
output or the content,
answer, data, graphs, analysis, etc.
Therefore beside that an ontological subjects of a composition is not only
represented by a
string of characters but also there would be additional vast information
available for the ontological
subject corresponding to its type/s of significance and relationship with
other ontological subjects
of the composition. Said additional information or data is learnt, through
implementing the methods
of current disclosure and the incorporated references herein, from the ways
these ontological
subjects being used or composed together to make up a composition or more
generally to form a
body of knowledge.
These information, data, or values of different objects of this disclosure
(e.g. association
strength measures, significance measure etc.) are placed in one or more data
structures which can be
representative of data arrays corresponding to vectors or matrix for
convenience of calculations by data
processing devices. The data processing devices to carry out the calculations,
storing, and data
transportation between the various part of one or more computer systems can be
selected from such
technologies such as electronic or optical based processors, semiconductor
based or quantum
computers, application specific processing devices and the like. Different
embodiments are given for
ease of calculations and processing the data of said one or more data
structures or vectors or matrices
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of ontological subjects and intelligent systems therefrom".
than can be implement with information, computing, or data processing systems
of certain processing
speeds and/or storage media access speed and capacities such as certain RAM
capacity, SSD, HD,
and/or optical memories and the like with required access time.
In this way the implicit information not recognizable, useable, or appreciable
by a human (due
to inherent biological limitations) can be extracted, stored and become
useable by a data processing
system or machine. Said data processing system or machine therefore will
become able to use its
superior processing speed and unmatched, by human, memory capacity or
environmental data
acquisition capabilities, to perform intelligent tasks. Examples of such
intelligent tasks could be, but
of course not limited to, conversing intelligently or evaluating a merit of a
composition, recognizing
visual objects, DNA analysis, knowledge discovery, automatic research and
discovery, or
composing an essay or a multimedia content, decision making, automatic
knowledge discovery,
controlling physical action/reaction of a machine ot its limbs, management of
tasks and sessions,
autonomous navigation, and in general such tasks that currently can only be
done by human being.
Intelligent beings (or artificially intelligent beings) of various kinds,
technologies, and forms, (e.g.
a humanoid robot maid, a genetically modified being, a transportation
intelligent beings such a an
autonomous car or an autonomous agricultural machine, a robotic explorer,
etc.), are exemplary
beneficiaries of implementing and employing the methods and systems of the
current disclosure.
More illustrating application system examples are further given to illustrate
the application of
the methods and systems of this disclosure in implementing neural networks
more efficiently and
processing images for such applications such as image recognition and
environmental knowledge
acquisition through visual data files.
All the methods, the systems and applications of this disclosure are used to
implement a real
and useful intelligent being which is capable performing intelligent tasks,
such as recognition of
objects, conversing with human client/user/master, reasoning, new knowledge
discovery,
navigation instructions, and all types of intelligent assistant systems,
either by embedding the
software and computable/executable codes into a desired system or by specific
physical building of
such systems.
According to another aspect of the present invention, there are provided
embodiments for
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
using the investigation methods of compositions and bodies of knowledge to
build and initialize a
machine learning neural networks and training such networks. Further
embodiments and exemplary
methods and systems are given for using the methods of this disclosure in
image and visual content
processing.
Further, in another aspect, the invention provides data processing systems
comprising
computer hardware, software, internet infrastructure, and other customary
appliances of an E-
business, cloud computing, distributed networks, and services to perform and
execute said methods
in providing a variety of services for a client/user's desired applications or
to provide a needed or
requested data to a human/agent client.
BRIEF DESCRIPTION OF THE DRAWINGS:
FIG 1: shows one exemplary block diagram of a system or a software artifact
that generates
various outputs from a body of knowledge or a composition according to one
embodiment of
the present invention.
FIG 2: shows one exemplary illustration of the concept of association strength
of a pair of
OSs according to one embodiment of the present invention.
FIG 3: shows one exemplary embodiment of a directed asymmetric network or
graph
corresponding to a composition of ontological subjects.
FIG 4: shows a block diagram of one preferred embodiment of the method and the
algorithm for calculating a number of exemplary "Value Significance Measures"
of different
types for the ontological subjects of a composition according to one
embodiment of the present
invention.
FIG 5: shows one exemplary block diagram of the method and the algorithm of
building
the "Ontological Subject Maps" (OSM) from the "Association Strength Matrix"
(ASM) which
is built for and from an input composition according to one embodiment of the
present
invention.
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of ontological subjects and intelligent systems therefrom".
FIGs. 6a, 6b, 6c, show the exemplary values and one way of representing the
values of the
different conveyers of the different types of the "value significance
measures".
FIG 7: shows one exemplary instance of implementing the formulations and
algorithm/s
illustrating one way of using the "participation matrix" (PM) and the
"association strength
matrix" (ASM) to calculate the two different types of the associations
strength of the OSs of
order 2 to the OSs of the order 1, according to one embodiment of the present
invention. This
Figure is to demonstrate the use of various VSM vectors (filters) in the
calculations.
FIG 8: is an block diagram the system and method of building at least two
participation
matrices and calculating VSM for lth order partition, OS1, to calculate the
"Value Significance
Measures" (VSM) of other partitions of the compositions, 0S1', and storing
them for further
use by the application servers according to one embodiment of the present
invention.
FIG 9: a block diagram of an exemplary application and the associated system
for ranking,
filtering, storing, indexing, clustering the crawled webpages, from the
internet or other
repositories, using "Value Significance Measures" (VSM) according to one
embodiment of the
present invention.
FIG 10 is an exemplary system of investigating module/s for investigation of
composition
of ontological subjects providing one or more desired result/data/output
according to one
embodiment of the present invention.
FIG 11-A: shows an exemplary application and realization of the disclosed
method using
a neural network in which the connection weight between neurons is adjusted
using the various
associations strengths measure according to the teaching of this disclosure a
block diagram of
an exemplary application for investigation of a body of news feeds.
FIG 11-B: illustrate an exemplary application and realization of the disclosed
method in
investigation visual compositions such as images/movie frames/pictures
composed of data of
corresponding to the constituent pixels. One exemplary choice of partitioning
an image is given.
Dark or white rectangles are indicatives of a pixels. All the investigation
methods of this
disclosure are therefore can be used to investigate and process an image or
set of images (e.g. a
video clip).
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of ontological subjects and intelligent systems therefrom".
FIG 11-C: is a block diagram of an exemplary application for investigation of
a body of
news feeds.
FIG 12: is another exemplary general system of using the investigator
providing various
services to the clients over a communication network (e.g. a private or
public) according to one
embodiment of the present invention. This embodiment shows exemplary general
architecture
of a system in which one or more of the blocks are optional and can be omitted
or one or more
blocks can be added.
FIG 13: is another exemplary block diagram of a composition investigation
service for a
client request for service according to one embodiment of the present
invention. One or more
functional modules can be still added to this embodiment and/or one or more of
the modules
can be removed or disabled.
FIG 14: An exemplary system of using the investigator providing various
services to the
clients in a private or public cloud environment according to one embodiment
of the present
invention.
FIG 15: another exemplary block diagram of a system of providing the various
ubiquitous
service to one or more clients over a network wherein the system can be either
localized or
distributed according to one embodiment of the present invention.
DETAILED DESCRIPTION:
I- INTRUDUCTION
A system of knowledge, here, means a composition or a body of knowledge in any
field,
narrow or wide, composed of data symbols such as alphabetical/numerical
characters, any array of
data, binary or otherwise, or any string of data etc. In this disclosure,
however, for the sake and ease
of explanation and comprehension, we mostly exemplify the compositions and
bodies of knowledge
with those that are expressed in natural language symbols with textual
characters
Accordingly, for instance a system of knowledge can be defined about the
process of stem cell
differentiation. In this example there are many unknowns that are desired to
be known. So consider
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
someone has collected many or all textual compositions about this subject.
Apparently the
collections contains many useful information about the subject that are
important but can easily be
overlooked by a human due to the limitations of processing capability and
memory capacity of
individuals' brains.
Another example of a body of knowledge according to the given definitions is a
picture or a
video signal. A picture or a video frame is consists of colored pixels that
have participated in a
picture to form and convey the information about the picture. Apparently some
colored pixels of
the picture are more significant or play a more distinguishing role in that
picture. Moreover their
combination or the way or the pattern that they participate together in any
small parts or segments
of that picture are also important in the way the pixels are conveying the
information about the
picture to an observer's eyes or a camera.
Yet another example of a composition or a body of knowledge could be a string
of genetic
codes, a DNA string, or a DNA strand, a whole genome, and the like.
Moreover any system, simple or complicated, can be identified and explained by
its constituent
parts and the relation between the parts. Additionally, any system or body of
knowledge can also
be represented by network/s or graph/s that shows the connection and relations
of the individual
parts of the system. The more accurate and detailed the identification of the
parts and their relations
the better the system is defined and designed and ultimately the better the
corresponding tangible
systems will function. Most of the information about any type of existing or
new systems can be
found in the body of many textual compositions. Nevertheless, these vast
bodies of knowledge are
unstructured, dispersed, and unclear for non expert in the field.
In the present invention, the purpose of the investigation is to model and
gain as much
information and knowledge about an unknown system comprised of ontological
subjects while the
source of the information about such a system is a given composition of
ontological subjects
wherein the composition is readable by a computer. Therefore, some information
about such an
unknown system is supposedly embedded in a body of knowledge or system of
knowledge or
generally in the given composition. The investigator, hence, will have to be
able to capture or
produce as much knowledge about the system from the information in the given
composition.
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of ontological subjects and intelligent systems therefrom".
Consequently, according to the present disclosure, the investigation is
performed according to
at least one significant/important aspect in the investigation of bodies of
knowledge (i.e.
compositions).
The "investigation important aspect" can, for example, be one or more of the
following goals:
1. identifying and recognizing the most significant constitutes parts of
the bodies of
knowledge according to at least one "significance aspect",
2. identifying the associated constituent parts of the bodies of knowledge,
and
3. identifying and/or finding (through discovery and/or reasoning) the
informative
constituent parts and informative combinations of the constituent parts of the
composition by, for
example, finding or composing the expressions that show a relationship between
two or more of
constituent parts of the bodies of knowledge.
Each of these "important aspect" or stages (1, 2, and 3 in the above) of the
investigation, of
course, can further be break down to two or more stages or steps or be
combined together to perform
a desirable investigation goal or to define the "investigation important
aspect".
For instance, according to one exemplary investigation method embodiment of
the present
invention, the "investigation important aspect" is to identify a relationship
between two or more
significant parts of the composition, the investigator may perform the
following:
1. identifying the most significant constituent part/s,
2. identifying the associated constituent parts of the bodies of knowledge,
and
3. finding or composing expressions that express the relationship between one
or more
significant parts having certain level of association to one or more of other
significant parts.
Therefore depends on the goal of the investigation the "investigation
important aspect" can
be defined and performed in more detailed processes. The present invention
gives a number of such
investigation goals and the methods of achieving the desired outcome.
Moreover, the present
invention provides a variety of tools and investigation methods that enables a
user to deal with
investigation of compositions of ontological subjects for any kind of goals
and any types of the
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of ontological subjects and intelligent systems therefrom".
composition.
As defined along this disclosure as well as the incorporated references
herein, the constituent
parts of the bodies of knowledge are called "Ontological Subjects" (OS). The
ontological subjects
further are grouped into different sets labeled with orders as will be
explained in the definition of
section of this disclosure too.
The "significance aspects", based on which the significances of the OSs of
compositions are
defined and calculated, are various that can be looked at. For instance one
"significance aspect"
could be an intrinsic significance of an OS which shows the overall or
intrinsic significance of an
OS in a body of knowledge. Another significance aspect is considered to be a
significant aspect in
relation or relative to one or more of the OSs of the body of knowledge.
Yet another significance aspect is considered to be an intrinsic novelty value
of an OS in a body
of knowledge or a composition. And yet another significance aspect is defined
as a relative or
relational novelty value of an OS related to one or more of the OSs of the
body of knowledge or a
composition.
Many other desirable significance aspect might be defined by different people
depends on the
application and the goal of the investigation of a composition or a body of
knowledge. Also any
combinations of such significance aspects can be regarded as a significance
aspect.
Accordingly a "significance aspect" is the orientation that one can use to
reason on how to put
a significance value on an ontological subject of a composition or a body of
knowledge.
In other words, a "significance aspect" is a qualitative quality that can
polarize or differentiate
the ontological subjects and be used to define "value significance measures"
and consequently
suggest or construct various value functions or significance weighting
functions on the ontological
subjects of a composition or a body of knowledge.
These functions, individually or in combination, therefore can be employed and
utilized to spot
and/or filter out the one or more ontological subjects of a composition or a
body of knowledge for
different purposes and applications or generally for investigation of bodies
of knowledge.
For instance and in accordance with one aspect of the present disclosure, for
the purpose of
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
investigation of the compositions of ontological subjects, a general form of
evaluating "value
significances" of the ontological subjects of a composition or a body of
knowledge or a network is
given along with a number of exemplified such value significances and their
applications. Such
investigation method/s will speed up the research process and knowledge
discovery, and design
cycles by guiding the users to know the substantiality of each part in the
system. Consequently
dealing with all parts of the system based on the value significance priority
or any other
predetermined criteria can become a systematic process and more yielding to
automation.
As will be explained in the next section, having constructed one or more
arrays of data
indicative of relations of constituent part, it will become necessary and
desirable to spot the
significant part and/or separate the parts that their significance is defined
in relation to a target part.
Thereby relational value significances are defined here. The relational value
significances are
instrumental in clustering a collection of composition or clustering
partitions of composition in
regards to one or more of a target OS or the parts of the system of knowledge.
Furthermore exemplary algorithms and systems are given to be used for
providing the
respective data and/or such application/s as one or more services to the
computer program agents
as well as human users.
Application of such methods and systems of investigations of compositions of
ontological
subjects would be very many and various. For example lets say after or before
a conference, with
many expert participants and many presented papers, one wants to compare the
submitted
contributing papers, draw some conclusions, and/or get the direction for
future research or find the
more important subjects to focus on, he or she could use the system, employing
the disclosed
methods, to find out the value significance of each concept along with their
most important
associations and interrelations. This is not an easy task for the individuals
who do not have many
years of experience and a deep and wide breadth of knowledge in the respective
domain of
knowledge.
Or consider a market research analyst who is assigned to find out the real
value of an enterprise
by researching the various sources of information. Or rank an enterprise among
its competitors by
identifying the strength and weakness of the enterprise constituent parts or
partitions. Or in another
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of ontological subjects and intelligent systems therefrom".
instance an enterprise, a blogger, a website owner, a content publisher, or a
Facebook subscriber
wants to find out the most valuable or the most interesting contents,
comments, or any parts of such
discussions. The investigation method of the present invention therefore can
provide such
information and knowledge with high confidence.
Many other consecutive applications such as searching engines, question
answering,
summarization, categorization, distillation, computer conversing, artificial
intelligence, genetics,
etc. can be performed, enhanced, and benefit from having an estimation of the
various "value
significances" of the partitions of the body of knowledge and a through
investigation method of
such compositions.
In order to describe the disclosure in details we first define a number of
terms that are used
frequently throughout this description. For instance, the information bearing
symbols are called
Ontological Subjects and are defined herein below, along with others terms, in
the definitions
sections.
I-I-DEFINITIONS:
This disclosure uses the definitions that were introduced in the US patent
application
12/755,415 filed on April-07-2010, and 12/939,112 filed on Nov-03-2010, which
are
incorporated herein as references, and are recited here again along with more
clarifying points
according to their usage in this disclosure and the mathematical formulations
herein.
1.
ONTOLOGICAL SUBJECT: symbol or signal referring to a thing (tangible or
otherwise) worthy of knowing about. Therefore Ontological Subject means
generally any string of
characters, but more specifically, characters, letters, numbers, words, binary
codes, bits,
mathematical functions, sound signal tracks, video signal tracks, electrical
signals, chemical
molecules such as DNAs and their parts, or any combinations of them, and more
specifically all
such string combinations that indicates or refer to an entity, concept,
quantity, and the incidences of
such entities, concepts, and quantities. In this disclosure Ontological
Subject/s and the abbreviation
OS or OSs are used interchangeably.
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of ontological subjects and intelligent systems therefrom".
2.
ORDERED ONTOLOGICAL SUBJECTS: Ontological Subjects can be divided
into sets with different orders depends on their length, attribute, and
function. Basically the order is
assigned to a group or set of ontological subjects having at least one common
predefined attribute,
property, attribute, or characteristic. Usually the orders in this disclosure
are denoted with alpha
numerical characters such as 0, 1, 2, etc or OS1, 0S2, etc. or any other
combination of characters
so as to distinguish one group or set of ontological subjects, having at least
one common predefined
characteristic, with another set or group of ontological subjects having
another at least one common
characteristic. This order/s will also be reflected in denoting/corresponding
the data objects or the
mathematical objects in the formulations to distinguish these data objects in
relation to their
corresponding ontological subject set or its order, as will be used and
introduced throughout this
disclosure. For instance, for ontological subjects of textual nature, one may
characterizes or label
letters as zeroth order OS, words or multiple word phrases as the first order,
sentences or multiple
word phrases as the second order, paragraphs as the third order, pages or
chapters as the fourth
order, documents as the fifth order, corpuses as the sixth order OS and so on.
As seen the order can
be assigned to a group or set of ontological subjects based on at least one
common predefined
characteristic of the members of the set. So a higher order OS is a
combination of, or a set of, lower
order OSs or lower order OSs are members of a higher order OS. Equally one can
order the genetic
codes in different orders of ontological subjects. For instance, the 4 basis
of a DNA molecules as
the zeroth order OS, the base pairs as the first order, sets of pieces of DNA
as the second order,
genes as the third order, chromosomes as the fourth order, genomes as the
fifth order, sets of similar
genomes as the sixth order, sets of sets of genomes as the seventh order and
so on. Yet the same can
be defined for information bearing signals such as analogue and digital
signals representing audio
or video information. For instance for digital signals representing a signal,
bits (electrical One and
Zero) can be defined as zeroth order OS, the bytes as first order, any sets of
bytes as third order, and
sets of sets of bytes, e.g. a frame, as fourth order OS and so on. Yet in
another instance for a picture
or a video frame, the pixels with different color can be regarded as first
order OS ( the RGB values
of a pixel can be regarded as zeroth order Oss) , a set whose members contain
two or more number
of pixels (e.g. a segment of a picture) can be regarded as OSs of second
order, a set whose members
contain of two or more such segments as third order OS, a set whose members
contain of two or
more such third order OSs as fourth order OS, a whole frame as fifth order OS,
and a number of
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
frames (like a certain period of duration of a movie such as a clip) as sixth
order and so on.
Therefore definitions of orders for ontological subjects are arbitrary set of
initial definitions that one
can stick to in order to make sense of the methods and mathematical
formulations presented herein
and being able to interpret the consequent results or outcomes in more
sensible and familiar
language."
More importantly Ontological Subjects can be stored, processed, manipulated,
and transported
by transferring, transforming, and using matter or energy (equivalent to
matter) and hence the
OS processing is an instance of physical transformation of materials and
energy.
3. COMPOSITION: is an OS composed of constituent ontological subjects of
lower
or the same order, particularly text documents written in natural language
documents, genetic
codes, encryption codes, data files, voice files, video files, and any mixture
thereof. A
collection, or a set, of compositions is also a composition. Therefore a
composition is in fact an
Ontological Subject of particular order which can be broken to lower order
constituent
Ontological Subjects. In this disclosure, the preferred exemplary composition
is a set of data
containing ontological subjects, for example a webpage, papers, documents,
books, a set of
webpages, sets of PDF articles, multimedia files, or even simply words and
phrases. Moreover,
compositions and bodies of knowledge are basically the same and are used
interchangeably in
this disclosure. Compositions are distinctly defined here for assisting the
description in more
familiar language than a technical language using only the defined OSs
notations.
4. PARTITIONS OF COMPOSITION: a partition of a composition, in general, is
a part or whole, i.e. a subset, of a composition or collection of
compositions. Therefore, a
partition is also an Ontological Subject having the same or lower order than
the composition as
an OS. More specifically in the case of textual compositions, parts or
partitions of a composition
can be chosen to be characters, words, sentences, paragraphs, chapters,
webpage, documents,
etc. A partition of a composition is also any string of symbols representing
any form of
information bearing signals such as audio or videos, texts, DNA molecules,
genetic letters,
genes, and any combinations thereof. However one preferred exemplary
definition of a partition
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
of a composition in this disclosure is word, sentence, paragraph, page,
chapters, documents, sets
of documents, and the like, or WebPages, and partitions of a collection of
compositions can
moreover include one or more of the individual compositions. Partitions are
also distinctly
defined here for assisting the description in more familiar language than a
technical language
using only the general OSs definitions.
5. SIGNIFICANCE MEASURE: assigning a quantity, or a number or feature or a
metric for an OS from a set of OSs so as to assist to distinguishing or
selecting one or more of
the OSs from the set. More conveniently and in most cases the significance
measure is a type
of numerical quantity assigned to a partition of a composition. Therefore
significance measures
are functions of OSs and one or more of other related mathematical objects,
wherein a
mathematical object can, for instance, be a mathematical object containing
information of
participations of OSs in each other, whose values are used in the decisions
about the constituent
OSs of a composition. For instance, "Relational, and/or associational, and/or
novel
significances" are one form or a type of the general "significance measures"
concept and are
defined according to one or more the aspect of interest and/or in relation to
one or more OSs of
the composition.
6. FILTRATION/SUMMARIZATION: is a process of selecting one or more OS
from one or more sets of OSs according to predetermined criteria with or
without the help of
value significance and ranking metric/s. The selection or filtering of one or
more OS from a set
of OSs is usually done for the purposes of representation of a body of data by
a summary as an
indicative of that body in respect to one or more aspect of interest.
Specifically, therefore, in
this disclosure searching through a set of partitions or compositions, and
showing the search
results according to the predetermined criteria is considered a form of
filtration/summarization.
In this view finding an answer to a query, e.g. question answering, or finding
a composition
related or similar to an input composition etc. is also a form of searching
through a set of
partitions and therefore are a form of summarization or filtration according
to the given
definitions here.
7. THE USAGE OF QUOTATION MARKS": throughout the disclosure several
compound names of concepts, variable, functions and mathematical objects and
their
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
abbreviations (such as "participation matrix", or PM for short, "Co-Occurrence
Matrix", or
COM for short, "value significance measure", or VSM for short, and the like)
will be
introduced, either in singular or plural forms, that once or more is being
placed between the
quotation marks (" ") for identifying them as one object (or a regular
expression that is used in
this disclosure frequently) and must not be interpreted as being a direct
quote from the literatures
outside this disclosure."
8. UNIVERSES OF COMPOSITIONS: Universe: in this disclosure "universe"
is
frequently used and have few intended interpretation: when "universe x" (x is
a number or letter
or word or combination thereof) is used it mean the universe of one or more
compositions, that
is called x, and contains none, one or more ontological subjects. By "real
universe" or "our
universe" we mean our real life universe including everything in it (physical
and its notions
and/or so called abstract and its notions) which is the largest universe
intended and exist.
Furthermore, "universal" refers to the real universe.
Furthermore, in the following description, numerous specific details are set
forth in order to
provide a thorough understanding of the present embodiments. It will be
apparent, however,
to one having ordinary skill in the art that the specific detail need not be
employed to practice
the present embodiments. In other instances, well-known materials or methods
have not been
described in detail in order to avoid obscuring the present embodiments.
1. Reference throughout this specification to "one embodiment", "an
embodiment", "one
example" or "an example" means that a particular feature, structure or
characteristic
described in connection with the embodiment or example is included in at least
one
embodiment of the present embodiments. Thus, appearances of the phrases "in
one
embodiment", "in an embodiment", "for instance", "one example" or "an example"
in
various places throughout this specification are not necessarily all referring
to the same
embodiment or example. Furthermore, the particular features, structures or
characteristics may be combined in any suitable combinations and/or sub-
combinations in
one or more embodiments or examples. In addition, it is appreciated that the
figures
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
provided herewith are for explanation purposes to persons ordinarily skilled
in the art and
that the drawings are not necessarily drawn to scale.
2. Embodiments in accordance with the present embodiments may be implemented
as an
apparatus, method, or computer program product. Accordingly, the present
embodiments
may take the form of an entirely hardware embodiment, an entirely software
embodiment
(including firmware, resident software, micro-code, etc.), or an embodiment
combining
software and hardware aspects that may all generally be referred to herein as
a "module"
or "system." Furthermore, the present embodiments may take the form of a
computer
program product embodied in any tangible medium of expression having computer-
usable program code embodied in the medium.
3. Any combination of one or more computer-usable or computer-readable media
may be
utilized. For example, a computer-readable medium may include one or more of a
portable computer diskette, a hard disk, a random access memory (RAM) device,
a read-
only memory (ROM) device, an erasable programmable read-only memory (EPROM or
Flash memory) device, a portable compact disc read-only memory (CDROM), an
optical
storage device, and a magnetic storage device. Computer program code for
carrying out
operations of the present embodiments may be written in any combination of one
or more
programming languages.
4. Embodiments may also be implemented in cloud computing environments. In
this
description and the following claims, "cloud computing" may be defined as a
model for
enabling ubiquitous, convenient, on-demand network access to a shared pool of
configurable computing resources (e.g., networks, servers, storage,
applications, and
services) that can be rapidly provisioned via virtualization and released with
minimal
management effort or service provider interaction, and then scaled
accordingly. A cloud
model can be composed of various characteristics (e.g., on-demand self-
service, broad
network access, resource pooling, rapid elasticity, measured service, etc.),
service models
(e.g., Software as a Service ("SaaS"), Platform as a Service ("PaaS"),
Infrastructure as a
Service ("IaaS"), and deployment models (e.g., private cloud, community cloud,
public
cloud, hybrid cloud, etc.).
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Patent Application of Hannid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
5. The flowchart and block diagrams in the flow diagrams illustrate the
architecture,
functionality, and operation of possible implementations of systems, methods,
and
computer program products according to various embodiments of the present
embodiments. In this regard, each block in the flowchart or block diagrams may
represent a module, segment, or portion of code, which comprises one or more
executable
instructions for implementing the specified logical function(s). It will also
be noted that
each block of the block diagrams and/or flowchart illustrations, and
combinations of
blocks in the block diagrams and/or flowchart illustrations, may be
implemented by
special purpose hardware-based systems that perform the specified functions or
acts, or
combinations of special purpose hardware and computer instructions. These
computer
program instructions may also be stored in a computer-readable medium that can
direct a
computer or other programmable data processing apparatus to function in a
particular
manner, such that the instructions stored in the computer-readable medium
produce an
article of manufacture including instruction means which implement the
function/act
specified in the flowchart and/or block diagram block or blocks.
6. As used herein, the terms "comprises," "comprising," "includes,"
"including," "has,"
"having," or any other variation thereof, are intended to cover a non-
exclusive
inclusion. For example, a process, article, or apparatus that comprises a list
of elements
is not necessarily limited to only those elements but may include other
elements not
expressly listed or inherent to such process, article, or apparatus.
7. Further, unless expressly stated to the contrary, "or" refers to an
inclusive or and not to an
exclusive or. For example, a condition A or B is satisfied by any one of the
following: A
is true (or present) and B is false (or not present), A is false (or not
present) and B is true
(or present), and both A and B are true (or present).
8. Additionally, any examples or illustrations given herein are not to be
regarded in any way
as restrictions on, limits to, or express definitions of any term or terms
with which they
are utilized. Instead, these examples or illustrations are to be regarded as
being described
with respect to one particular embodiment and as being illustrative only.
Those of
ordinary skill in the art will appreciate that any term or terms with which
these examples
or illustrations are utilized will encompass other embodiments which may or
may not be
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
given therewith or elsewhere in the specification and all such embodiments are
intended
to be included within the scope of that term or terms. Language designating
such
nonlimiting examples and illustrations includes, but is not limited to: "for
example," "for
instance," "e.g.," and "in one embodiment."
Now the invention is disclosed in details in reference to the accompanying
Figures and
exemplary cases and embodiments in the following subsections.
II-DESCRIPTION
The methods and systems that are devised here is to solve the proposed problem
of
investigating compositions of ontological subjects through algorithmic
manipulating and assigning
and calculating various "value significance" quantities to the constituent
ontological subjects of a
composition or a network of ontological subjects. It is further to disclose
the methods of measuring
the significance of the value/s so that the right "Value Significance
Measure/s (VSM)", can be
defined, synthesized, and be calculated for a desired aspect of investigation
and be used for further
processing of many related applications or other measures.
.
The methods and systems of the present invention and can be used for
applications ranging
from document classification, search engine document retrieval, news analysis,
knowledge
discovery and research trajectory optimization, question answering, computer
conversation, spell
checking, summarization, categorizations, categorization, clustering,
distillation, automatic
composition generation, genetics and genomics, signal and image processing, to
novel applications
in economical systems by evaluating a value for economical entities, crime
investigation, financial
applications such as financial decision making, credit checking, decision
support systems, stock
valuation, target advertising, and as well measuring the influence of a member
in a social network,
and/or any other problem that can be represented by graphs and for any group
of entities with some
kind of relations or association.
Although the methods are general with broad applications, implications, and
implementation
strategies and technique, the disclosure is described by way of specific
exemplary embodiments to
consequently describe the methods, implications, and applications in the
simplest forms of
embodiments and senses.
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PatentApplication of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
Also since most of human knowledge and daily information production is
recorded in the form
of text (or it can be converted or represented with textual/numerical
characters) the detailed
description is focused on textual compositions to illustrate the teachings and
the methods and the
systems. In what follows the invention is described in several sections and
steps which in light of
the previous definitions would be sufficient for those ordinary skilled in the
art to comprehend and
implement the methods, the systems and the applications thereof. In the
following section we first
set the mathematical foundation of the disclosed method from where we launch
into introducing
several "value significance measures" (VSMs) and ways of calculating them and
their applications.
We explain the method/s and the algorithms with the step by step formulations
that is easy
to implement by those of ordinary skilled in the art and by employing computer
programming
languages and computer hardware systems that can be optimized or customized by
build or
design of hardware to perform the algorithm efficiently and produce useful
outputs for various
desired applications.
II-I PARTCIPATION MATRIX BUILDING FOR A COMPOSITION
Assuming we have an input composition of ontological subjects, e.g. an input
text, the
"Participation Matrix" (PM) is a matrix indicating the participation of one or
more ontological
subjects of particular order in one or more partitions of the composition. In
other words in terms
of our definitions, PM indicate the participation of one or more lower order
OS into one or more
OS of higher or the same order. PM/s are the most important array of data in
this disclosure that
contains the raw information from which many other important functions,
information, features,
and desirable parameters can be extracted. Without intending any limitation on
the value of PM
entries, in the exemplary embodiments throughout most of this disclosure
(unless stated
otherwise) the PM is a binary matrix having entries of one or zero and is
built for a composition
or a set of compositions as the following:
1. break
the composition to desired numbers of partitions. For example, for a text
document, break the documents into chapters, pages, paragraphs, lines, and/or
sentences, words
etc. and assign an order number (e.g. 0,1,2,3..etc) to any set of similar
partitions, i.e. the ordered
ontological subjects,
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of ontological subjects and intelligent systems therefrom".
2. select a desired N number of OSs of order k and a desired M number of
OSs of
order 1 (these OSs are usually the partitions of the composition from the step
1) according to
certain predetermined criteria, and;
3. construct aNxM matrix in which the ith raw (Re) is a vector (e.g. a
binary vector),
with dimension M, indicating the presence of the ith OS of order k, (often
extracted from the
composition under investigation), in the OSs of order 1, (often extracted from
the composition
under investigation or sometimes from another referenced composition), by
having a nonzero
value, and not present by having the value of zero.
We call this matrix the "Participation Matrix" (usually a binary matrix) of
the order kl
(PMki) which can be represented as:
OS1 ... OS)4
0 SI' pm = = = p m \
lM
pmkt _ (1)
OSk P171 ki 1111m/
where OSpk is the pth OS of the kth order (p = 1 N), 0Sql is the qth OS of the
/th order (q =
1 ... M), usually extracted from the composition, and, according to one
embodiment of this
invention, PM A = 1 if OS have have participated, i.e. is a member, in the OS
4 and 0 otherwise.
The desired criteria, in the step 2 above, can be, for instance, to only
select the content words
or select certain partitions having certain length or, in another instance,
selecting all and every
word or character strings and/or all the partitions.
The participating matrix of order lk, i.e. PM, can also be defined which is
simply the transpose
of PM"1 whose elements are given by:
PM = Dul'icqP kl (2).
PQ I
Accordingly without limiting the scope of invention, the description is given
by exemplary
embodiments using the general participation matrix of the order kl , i.e the
PMk1 in which k <
1.
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of ontological subjects and intelligent systems therefrom".
Furthermore PM carries much other useful information. For example using binary
PMs,
one can obtain a participation matrix in which the entries are the number of
time that a particular
OS (e.g. a word) is being repeated in another partitions of particular
interest (e.g. in a document)
one can readily do so by, for instance, the following:
pm_R is = pm12 x pm25 (3)
wherein the PM_R15 stands for participation matrix of OSs of order 1 (e.g.
words) into OSs of
order 5 (e.g. the documents) in which the nonzero entries shows the number of
time that a word
has been appeared in that document (however the possible repetition of a word
in an OS of order
2, e.g sentences, will not be accounted for here). Another applicable example
is using PM data
to obtain the "frequency of occurrences" of ontological subjects in a given
composition by:
FOl = Ei pm/6! (4)
wherein the FO" is the frequency of occurrence of OSs of order k, i.e. OSt, in
the OSs of order
1, i.e. the OS' . The latter two examples are given to demonstrate on how one
can conveniently
use the PM and the disclosed method/s to obtain many other desired data or
information.
More importantly, from PM"i one can arrive at the "Co-Occurrence Matrix" COM"
for
OSs of the same order as follow:
COM" = pmkt * (pmkl)T (5),
where the" T" and" * "show the matrix transposition and multiplication
operation respectively.
The COM is a NxN square matrix. This is the co-occurrences of the ontological
subjects of
order k in the partitions (ontological subjects of order 1) within the
composition and is one
indication of the association of OSs of order k evaluated from their pattern
of participations in
the OSs of order 1 of the composition. The co-occurrence number is shown by
comik)1 which is
an element of the "Co-Occurrence Matrix (COM)" and (in the case of binary PMs)
essentially
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of ontological subjects and intelligent systems therefrom".
showing that how many times OS t and St has participated jointly into the
selected OSs of the
order 1 of the composition. Furthermore, COM can also be made binary, if
desired, in which
case only shows the existence or non-existence of a co-occurrence between any
two OSk
The importance of the "co-occurrence matrix" as defined in this disclosure is
that it carries
or contain the information of relationship and associations of the OSs of the
composition which
is further utilized in some embodiments of the present invention.
It should be noticed that the co-occurrences of ontological subjects can also
be obtained by looking at,
for instance, co-occurrences of a pair of ontological subject within certain
(i.e. predefined) proximities
in the composition (e.g. counting the number of times that a pair of
ontological subjects have co-occurred
within certain or predefined distances from each other in the composition) as
was used in the
incorporated reference the US patent application Ser. No. 12/179,363.
Similarly there are other ways to
count the frequency of occurrences of an ontological subjects (i.e. the FO).
However the preferred
embodiment is an efficient way of calculating these quantities or objects and
should not be construed as
the only way implementing the teachings of the present invention. The repeated
co-occurrences of a
pair of ontological subjects within certain proximities is an indication of
some sort of association (e.g. a
logical relationship) between the pair or else it would have made no sense to
use them together in one
or more partitions of the composition.
Those skilled in the art can store the information of the PMs, and also other
mathematical
objects of the present invention, in equivalent forms without using the notion
of a matrix. For
example each raw of the PM can be stored in a dictionary, or the PM be stored
in a list or lists
in list, or a hash table, or a SQL database, or any other convenient objects
of any computer
programming languages such as Python, C, Perl, Java, etc. Such practical
implementation
strategies can be devised by various people in different ways. Moreover, in
the preferred
exemplary embodiments the PM entries are binary for ease of manipulation and
computational
efficiency.
However, in some applications it might be desired to have non-binary entries
so that to
account for partial participation of lower order ontological subjects into
higher orders, or to
show or preserve the information about the location of
occurrence/participation of a lower order
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of ontological subjects and intelligent systems therefrom".
OS into a higher order OSs, or to account for a number of occurrences of a
lower OS in a higher
OS etc., or any other desirable way of mapping/converting or conserving some
or all of the
information of a composition into a participation matrix. In light of the
present disclosure such
cases can also be readily dealt with, by those skilled in the art, by slight
mathematical
modifications of the disclosed methods herein.
Furthermore, as pointed out before, those skilled in the art can store,
process or represent the
information of the data objects of the present application (e.g. list of
ontological subjects of
various order, list of subject matters, participation matrix/ex, association
strength matrix/ex, and
various types of associational, relational, novel, matrices, various value
significance measures,
co-occurrence matrix, participation matrices, and other data objects
introduced herein) or other
data objects as introduced and disclosed in the incorporated references (e.g.
association value
spectrums, value significance measures, ontological subject map, ontological
subject index, list
of authors, and the like and/or the functions and their values, association
values, counts, co-
occurrences of ontological subjects, vectors or matrix, list or otherwise, and
the like etc.) of the
present invention in/with different or equivalent data structures, data arrays
or forms without
any particular restriction.
For example the PMs, ASMs, OSM or co-occurrences of the ontological subjects
etc. can be
represented by a matrix, sparse matrix, table, database rows, no sql
databases, JSON,
dictionaries and the like which can be stored in various forms of data
structures. For instance
each part, section, or any subset of the objects of the current disclosure
such as a PM, ASM,
OSM, RNVSM, NVSM, and the like or the ontological subject lists and index, or
knowledge
database/s can be represented and/or stored in one or more data structures
such as one or more
dictionaries, one or more cell arrays, one or more row/columns of an SQL
database, or by any
implementation of No SQL database/s of different technologies or methods etc.,
one or more
filing systems, one or more lists or lists in lists, hash tables, tuples,
string format, zip format,
sequences, sets, counters, JSON, or any combined form of one or more data
structure, or any
other convenient objects of any computer programming languages such as Python,
C, Pen,
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
Java., JavaScript etc. Such practical implementation strategies can be devised
by various people
in different ways.
The detailed description, herein, therefore describes exemplary way(s) of
implementing the
methods and the system of the present invention, employing the disclosed
concepts. They should
not be interpreted as the only way of formulating the disclosed concepts,
algorithms, and the
introducing mathematical or computer implementable objects, measures,
parameters, and variables
into the corresponding physical apparatuses and systems comprising
data/information processing
devices and/or units, storage device and/or computer readable storage media,
data input/output
devices and/or units, and/or data communication/network devices and/or units,
etc.
The processing units or data processing devices (e.g. CPUs) must be able to
handle various
collections of data. Therefore the computing or data processing units to
implement the system have
compound processing speed equivalent of one thousand million or larger than
one thousand million
instructions per second and a collective memory, or storage devices (e.g.
RAM), that is able to
store large enough chunks of data to enable the system to carry out the task
and decrease the
processing time significantly compared to a single generic personal computer
available at the time
of the present disclosure."
The data/information processing or the computing system that is used to
implement the method/s,
system/s, and teachings of the present invention comprises storage devices
with more than 1 (one)
Giga Byte of RAM capacity and one or more processing device or units ( i.e.
data processing or
computing devices, e.g. the silicon based microprocessor, quantum computers
etc.) that can
operate with clock or instruction speeds of higher than 1 (one) Giga Hertz or
with compound
processing speeds of equivalent of one thousand million or larger than one
thousand million
instructions per second (e.g. an Intel Pentium 3, Dual core, i3, i7 series,
and Xeon series processors
or equivalents or= similar from other vendors, or equivalent processing power
from other
processing devices such as quantum computers utilizing quantum computing
devices and units)
are used to perform and execute the method once they have been programmed by
computer
readable instruction/codes/languages or signals and instructed by the
executable instructions.
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Patent Application of Hannid Hatanni-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
Additionally, for instance according to another embodiment of the invention,
the computing or
executing system includes or has processing device/s such as graphical
processing units for visual
computations that are for instance, capable of rendering, synthesizing, and
demonstrating the
content (e.g. audio or video or text) or graphs/maps of the present invention
on a display (e.g.
LED displays and TV, projectors, LCD, touch screen mobile and tablets
displays, laser projectors,
gesture detecting monitors/displays,3D hologram, and the like from various
vendors, such as
Apple, Samsung, Sony, or the like etc.) with good quality (e.g. using a NVidia
graphical processing
units).
Also the methods, teachings and the application programs of the presents
invention can be
implement by shared resources such as virtualized machines and servers (e.g.
VMware virtual
machines, Amazon Elastic Beanstalk, e.g. Amazon EC2 and storages, e.g. Amazon
S3, and the
like etc. Alternatively specialized processing and storage units (e.g.
Application Specific
Integrated Circuits ASICs, field programmable gate arrays (FPGAs) and the
like) can be made and
used in the computing system to enhance the performance and the speed and
security of the
computing system of performing the methods and application of the present
invention.
Moreover several of such computing systems can be run under a cluster,
network, cloud, mesh or
grid configuration connected to each other by communication ports and data
transfers apparatuses
such as switches, data servers, load balancers, gateways, modems, internet
ports, databases servers,
graphical processing units, storage area networks (SANs) and the like etc. The
data communication
network to implement the system and method of the present invention carries,
transmit, receive,
or transport data at the rate of 10 million bits or larger than 10 million
bits per second;"
Furthermore the terms "storage device, "storage", "memory", and "computer-
readable storage
medium/media" refers to all types of no-transitory computer readable media
such as magnetic
cassettes, flash memories cards, digital video discs, random access memories
(RAMS s), Bernoulli
cartridges, optical memories, read only memories (ROMs), Solid state discs,
and the like, with the
sole exception being a transitory propagating signal."
The detailed description, herein, therefore uses a straightforward
mathematical notions and
formulas to describe exemplary ways of implementing the methods and should not
be
interpreted as the only way of formulating the concepts, algorithms, and the
introduced
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
measures and applications. Therefore the preferred or exemplary mathematical
formulation here
should not be regarded as a limitation or constitute restrictions for the
scope and sprit of the
invention which is to investigate the bodies of knowledge and compositions
with systematic
detailed accuracy and computational efficiency and thereby providing effective
tools, products
and application in knowledge discovery, scoring/ranking, decision making,
navigation,
conversing, man/Machine collaboration and interaction, filtering or
modification of partitions
of a body of knowledge, string processing, information processing, signal
processing and the
like.
Having constructed the PM, we now launch to explain the methods of defining
and
evaluating the "value significances" of the ontological subjects of the
compositions for various
important measures of significance. One of the advantages and benefits of
transforming the
information of a composition into participation matrices is that once we
attribute something to
the OSs of particular order then we can evaluate the merit of OSs of another
order in regards to
that attribute using the PMs. For instance, if we find words of particular
importance in a textual
composition then we can readily find the most important sentences of the
composition wherein
the most important sentences contain the most important words in regards to
that particular
importance measure or aspect. Moreover, as will be shown, the calculations
become
straightforward, language independent and computationally very efficient
making the method
practical, accurate to the extent of our definitions, and scalable in
investigating large volumes
of data or large bodies of knowledge.
The investigation method/s and the algorithm/s are now explained in the
following
sections and subsections with the step by step formulations that is easy to
implement by those
of ordinary skilled in the art and by employing computer programming languages
and computer
hardware systems that can be optimized or customized by build or hardware
design to perform
the algorithm efficiently and produce useful outputs for various desired
applications.
II-II VALUE SIGNIFICANCE MEASUERS
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
This section begins to concentrate on value significance evaluation of a
predetermined
order OSs by several exemplary embodiments of the preferred methods to
evaluate the value of
an OS of the predetermined order, within a same order set of OSs of the
composition, for the
desired measure of significance.
Using these mathematical objects various measures of value significances of
OSs in a body
of knowledge or a composition (called "value significance measure") can be
calculated for
evaluating the value significances of OSs of different orders of the
compositions or different
partitions of a composition. Furthermore, these various measures (usually have
intrinsic
significances) are grouped in different types and number to distinguish the
variety and
functionalities of these measures.
The first type of a "value significance measure" is defined as a function of
"Frequency of
Occurrences" of OS!' is called here FO' and can be given by:
vsmAk. II = f1(FOik11), i = 1,2, ...N (6)
wherein FOikiiis obtained by counting the occurrences of OSs of the particular
order, e.g.
counting the appearances of particular word in the text or counting its total
occurrences in the
partitions, or more conveniently be obtained from the COM (the elements on
the main
diagonal of the COMkil) or by using Eq. 4, or any other way of counting the
occurrences of OS!'
in the desired partitions of the composition.
Moreover the ft in Eq. 6 is a predetermined function such that fi(x) might be
a liner
function (e.g. ax+b), a power of x function (e.g. x3 or x .53), a logarithmic
function (e.g.
1/7og2(x)), or 1/x function, etc.
Accordingly, a vsm_1_1ikl1, (stands for number one of type one "value
significance
measure") for instance, can be defined as:
vsm_1_1kil 1 = c. Fak 11 (7)
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
wherein c is a constant or a pre-assigned vector. The vsm_llik II of Eq. 7
gives a high value to
the most frequent OSk . In another situation or some applications if, for a
desired aspect, less
frequent OSs are of more significance one may use the following vsm_1_2ki
(number 2 of type
1 vsm)
kit c
vsm_1_21 = , i = 1,2, N (8)
(Foi
Furthermore, another type of vsm_411 is defined as a function of the
"Independent
Occurrence Probability" (10P) in the partitions such as:
kit. _
vsm_2 = 12v, ), ¨ 1 N (9)
wherein the independent occurrence probability (iop) may conveniently be given
by:
kit FO:
(iopi ) = = , i = 1 N (10)
and f2 is a predetermined function. For instance a vsm_2_1,k. II (i.e. the
number 1 type 2 vsm)
can be defined as:
vsm_2_1ki it = ¨log2(iop,!11), i = 1 N (11)
This measure gives a high value to those OSs of order k of the composition
(e.g. the words when
k=1) conveying the most amount of information as a result of their occurrence
in the
composition. Extreme values of this measure can point to either novelty or
noise.
Still, another type of vsm _411 is defined as a function of the "co-occurrence
of an OSk
with others as:
vsm_3 = f3(comi) . _ ), ¨ 1 ...N (12)
wherein the comk.11 is the co-occurrences of OSk and OS!' and f3 is a
predetermined function.
For instance a vsm_3 can be defined as:
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
kit kit
vsm_3_11 = f3(comij ) = Ei comiicjit , i = 1 N (13).
This measure gives a high value to those frequent OSs of order k that have co-
occurred
with many other OSs of order k in the partitions of order 1.
This measure (Eq. 13) once combined with other measures can yet provide other
measures.
kitkit
For instance when it is being divided by the vsm_1_1i of Eq. 7, (e.g. being
divided by F01 ),
the resultant measure can indicates the diversity of occurrence of that OS.
Therefore, this
particular combined measure usually gives a high value to the generic words
(since generic
words can occur with many other words). Once the generic words excluded from
the list of OSs
of the order k then this measures can quickly identifies the main subject
matter of a composition
so that it can be used to label a composition or for classification,
categorization, clustering, etc.
Accordingly, more vsm_xtk. 11 can be defined using the one or more of the
other vsmikl I or
the variables. For instance one can define a vsm_xikli of type 4 (x=4) as
function of vsm_1_2ki 11
given by Eq. 8 and comikjil as the following:
kil k kit T
125111_4_1kit = f4(12S171_1_2i , COMi) = Ei (comii .vsm_1_2it i ) = (1/FO) x
COM, i,j = 1 N
(14)
wherein "T" stands for matrix or vector transposition operation and wherein we
substitute the
vsm_1_2jkli from Eq. 8 into Eq. 12 or 14. This measure also points to the
diversity of the
participations of the respective OS especially when COM is made digital.
For mathematical accuracy it is noticed that in our notation the index "i"
refers to the row
number and the index "j" refers to the column number therefore the matrices
with only the
subscript of "i" usually are the column vectors and the matrices with only the
subscript of "j"
usually are row vectors.
In a similar fashion there could be defined, synthesized, and be calculated
various vsm_xikli
(x=1,2,3,..) vectors for OS/' that are indicatives of one or more
significances aspect's of an OSifc
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
in the composition or the BOK. These groups of vsm_x,k. 11 generally refer to
the intrinsic value
significance of an OS in the BOK.
These "value significance measures" (vsm_xt) are more indicative of intrinsic
importance
or significances of lower order constituent part that can be use to separate
one or more of the
these OSs for variety of applications such as labeling, categorization,
clustering, building maps,
conceptual maps, ontological subject maps, or finding other significant parts
or partitions of the
composition or the BOK. For instance as disclosed in the incorporated
references the vsm_xik 11
can readily be employed to score a set of document or to select the most
import parts or
partitions of a composition by providing the tools and objects to weigh the
significances of parts
or partitions of a BOK.
Accordingly, from the vsm_xt vectors one can readily proceed to calculate the
vsm_x
of other OS of different order (i.e. an order 1) utilizing the participation
matrices PMk1 by a
multiplication operation by:.
vsm_xi/pa = (vsm_xt)T x pmJ j = 1,2,.. M and i = 1,2, ... N
(15)
wherein vsm x.Iva is the type x value significance of OSs of order 1 obtained
from the data of
the PM/CL. An instance meaning of OS of order 1 for a textual composition or a
BOK is a sentence
(e.g. 1=2) , a paragraph (e.g. 1=3) or a document (1=5). The vsm_xfilki
thereafter can be utilized
for scoring, ranking, filtering, and/or be used by other functions and
applications based on their
assigned value significances.
Generally, many other "value significant measures" can be constructed or
synthesized as
functions of other "value significance measures" to obtain a desired new value
significance
measure.
Therefore, from the disclosure here, it becomes apparent as how various
filtering functions
can be synthesized utilizing the participation matrix information of different
orders and other
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
derivative mathematical objects. The method is thereby easily implemented and
is process
efficient.
An immediate application of the theory and the associated methods, systems,
and
applications are instrumental in processing of natural languages composition
and building the
artificial intelligences capable of interacting with humans in an intelligent
manner.
II-III THE ASSOCIATION STRENGTH
This section look into another important attributes of the ontological
subjects of a
composition that is instrumental and desirable in investigating the
composition of ontological
subjects.
According to the theoretical discoveries, methods, systems, and applications
of the present
invention, the concept and evaluation methods of "association strengths"
between the
ontological subjects of a composition or a BOK play an important role in
investigating,
analyzing and modification of compositions of ontological subjects.
Accordingly, the "association strength measures" are introduced and disclosed
here. The
"association strength measures" play important role/s in many of the proposed
applications and
also in calculating and evaluating the different types of "value significance
evaluation" of OSs
of the compositions. The values of an "association strength measure" can be
shown as entries
of a matrix called herein the "Association Strength Matrix (ASMk11)".
The entries of ASM" is defined in such a way to show the concept and rational
of
association strength according to one exemplary general embodiment of the
present invention
as the following:
xii kit
asmi,i = f (comij ,vsm_xi ,vsm_yi) i,j = 1. . N, x,y = 1,2, ... (16),
x/
where asmi is the "association strength" of OSik to 05!` of the composition
and f is a
predetermined or a predefined function, comik)I are the individual entries of
the COM"
showing the co-occurrence of the OSik and 05ik in the partitions or OS1 , and
the vsni_xt and
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
vsm_yji. are the values of one of the "value significance measures" of type x
and type y of the
OSt and 051c respectively, wherein the occurrence of OSk is happening in the
partitions that
are OSs of order 1. Usually the vsm_xt and/or the vsm_yf are the same as
vsm_xik and/or the
vsm_yjkll which means it has been calculated from the participation data of
the OSk in the OSs
of order 1.
Accordingly having selected the desired form of the function f and introducing
the
exemplary quantities from Eq. 6, and/or 9 and/or Eq. 12 into Eq. 16 the value
of the
corresponding "association strength measure" can be calculated.
Referring to FIG 2 here, it shows one definition for association of two or
more OSs of a
composition to each other and shows how to evaluate the strength of the
association between
each two OSs of composition. In FIG 2 the "association strength" of each two
OSs has been
defined as a function of their co-occurrence in the composition or the
partitions of the
composition, and the value significances of each one of them.
FIG 2, moreover shows the concept and rational of this definition for
association strength
according to this disclosure. The larger and thicker elliptical shapes are
indicative of the value
significances, e.g. probability of occurrences, of OSt and OSik in the
composition that were
driven from the data of PMk1 and wherein the small circles inside the area is
representing the
OS1 s of the composition. The overlap area shows the common 051 between the
OSt and 0S7
in which they have co-occurred, i.e. those partitions of the composition that
includes both 0S7
and OSl !`. The co-occurrence number is shown by com9, which is an element of
the "Co-
Occurrence Matrix (COM)" introduced before (Eq. 5).
kil
The various asTTl can be grouped into types and number in order to
distinguish them
from other measures in a similar fashion in labeling and naming the VSMs in
the previous
subsection. Consequently few exemplary types of "association strength
measures", asmik,Iii ,
are given below:
asm 1 11'11 = cam ¨ 1 N (17)
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
kit kit kit
asm - 2 -1. . = comL=j /VSM_Xi = 1. . N , x,y = 1,2, ... (18)
kit VS771_37 = kit
asm_3_11 = ________ )kii comii i,j = 1.. N , x,y = 1,2, (19)
vsm_x
It is important to notice that the association strength defined by Eq. 16, is
not usually
symmetric and generallyasmik.,Iii # asm. Therefore, one important aspect of
the Eq. 16 to be
pointed out here is that associations of OSs of the compositions are not
necessarily symmetric
and in fact an asymmetric "association strength measure" is more rational and
better reflects the
actual semantic relationship situations of OSs of the composition.
For instance in the patent application 12/939,112 the exemplary and preferred
"association
strength measure" that in this application is labeled as asm_3_2ik_+Ii1, (it
reads as number 2 type
3 "association strength measure") to make it distinguishable from other
measures, was defined
as:
kil kilku
corn ij corn .iop
asm - 3 - 20 = c (iopki7 )= c __ 1,11 , = 1.. N (20)
iop
kii
I.
where c is a predetermined constant, or a pre-assigned value vector, or a
predefined function
of other variables in Eq. 20, comikjit are the individual entries of the COM?*
showing the co-
kit
occurrence of the OS!' and OSii-c in the partitions of order 1, and the and
iopii5it are the
"independent occurrence probability" of OS t and OSic in the partitions
respectively, wherein
the occurrence is happening in the partitions that are OSs of order 1. In a
particular case, it can
be seen that in Eq. 20, the un-normalized "association strength measure" of
each OS with itself
is proportional to its frequency of occurrence (or self occurrence).
This exemplary choice of definition for "association strength measure", i.e.
Eq. 20, is
further illustrated here. In fact Eq. 20 basically states that if a less
popular OS co-occurred with
a highly popular OS then the association of the less poplar OS to the highly
popular OS is much
stronger than the association of the highly popular OS with the less popular
OS (remembering
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
the co-occurrence is a symmetric). That make sense, since the popular OSs
obviously have many
associations and are less strongly bounded to anyone of them so by observing a
high popular
OSs one cannot gain much upfront information about the occurrence of less
popular OSs.
However observing occurrence of a less popular OSs having strong association
to a popular OS
can tip the information about the occurrence of the popular OS in the same
partition, e.g. a
sentence, of the composition.
In another instance it may be more desirable to have defined the association
strength
measure as:
111
kll corn..
asm_2_21,j = c 1 j = 1.. N (21)
Lopi
kit
This asm - 2 -2. measure effectively expressing that association of an OS t to
another one,
say OS'}, is stronger when the co-occurrences of them is high and the
probability of occurrence
of OSt is low. In other words if an OS is occurring less frequently and
whenever it has occurred
it has appeared more often with one particular OS then the association bond of
the less
frequently occurring OS is strongest with the particular OS that has co-
occurred with, the most.
In the other way for a given co-occurrence number for a particular OS, say
0,S1', it's highest
associated bond is from the OS with less independent occurrence probability.
Mathematically,
in fact, the asm_2_2ik,111 is the column normalized version of the
asm_3_2tilLij of Eq. 20 (when
c=1/M in Eq. 21 and assuming binary PM ) and is more useful in some instances
and
applications.
This particular association strength measure can reveal a strong relationship
from a less
significant OS to the one who has co-occurred the most and is a useful measure
to hunt for some
types of novelty.
Yet in another instance an application/s is found for the following
association strength
definition:
= c comikjil. iop1 i,j = 1..N (22).
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of ontological subjects and intelligent systems therefrom".
The asm_4_1kiLli attributes the strongest association bond from a first OS,
say OS, to a
second OS, say OSJ!' , when the product of their co-occurrences and the
independent probability
of occurrence of the second OS is the highest. This association strength
measure usually is
useful for discovering the real association of two important or significant
OSs of the
composition.
And yet further, this measure can be defined to hunt for mutual associations
bonds such as
word phrases as the following:
ku (comkii1)2
asm 2 3. = c ___________
- - kii,
J = 1.. N (23)
This measure of association strength (i.e. Eq. 23) is symmetric and gives a
high value to
those pairs of OSs that frequently co-occur with each other such as word
phrases. This becomes
equal to 1 (assuming c=1 in Eq. 23) when two words have always co-occurred
with each other.
These are few exemplary but useful types of association strength measures
which are found
to be instrumental in analyzing and investigation of a composition of
ontological subjects.
However by Eq. 16 it can be seen that there could be defined, synthesized and
calculate
numerous other association strength measures. Furthermore considering that
comik)1 is also one
type of "association strength measure" therefore Eq. 16 can be further
generalized as:
kil
asm_x2i,1 = F(asm_x1,vsm_xi , vsm_y j = 1.. N ,x,y = 1,2, ..., x1, x2
=
1,2, ... (24),
wherein F is a predetermined function and xl and x2 refer to different types
of association
strength measures and xi and yi refer to one of the "value significance
measures" of the
different types of "value significance measures". To illustrate this, one can
see that the
kit kit kit
asm 3 2. can be expressed versus the asm 2 2. . (Eq. 21) and the vsm 1. (Eq.
7) as:
- - E->j - - 1-v -
kit kit kit
asm 3 2. = c.asm 2 2. vsm 1. (25)
- - - - t-v= - I
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of ontological subjects and intelligent systems therefrom".
wherein c is a constant and "." indicates an element-wise multiplication of
two vectors and
wherein Eqs. 7, 10, 20, 21 were combined to derive the Eq. 25.
These illustrating examples are given to demonstrate that with the concept of
"value
significance" and "association strengths" there will be various ways to
synthesize, perform,
calculate and obtain the desired association strength for the particular
application by those
skilled in the art.
II-III-I-CROSS ASSOCIATION STRENGTH MEASURES
Also importantly from the one or more of the "association strength measures"
one can go
on and define a measure for evaluating the hidden association strength of OS
of order k even
further by:
ASM_x3k11 = (Asm_xi) kit\T
x ASM_x2k (26)
wherein ASM_x3k11 stands for type x3 "association strength measure" which is
basically a N x
N matrix. The Eq. 26 takes into account the transformative or hidden
association of OSs of order
k (e.g. words of a textual composition or BOK) from one asm measure and
combines with the
information of another or the same asm measure to gives another measure of
association that is
not very obvious or apparent from the start. This type of measure therefore
takes into account
the indirect or secondary associations into account and can reveal or being
used to suggest new
or hidden relationships between the OSs of the compositions and therefore can
be very
instrumental in knowledge discovery and research.
Eq. 26 can, in fact, be interpreted as "cross-association strength" between
ontological subjects in general
with the same or different association strength measure in mind.
When we use the same type of association strength measure, in yet another
exemplary and effective way
we introduce another measure of association calling it "cross-association
strength measure" or CASM
for short which is defined as:
CASM = (ASM x ASMT) (26.1)
Wherein, in here, ASM, is one of the desired types of the association matrix
and "T' stands for matrix
transposition operation and "x" indicates matrix multiplications. Eq. 26.1 is
one particular case for the
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
general concept of "cross-association strength measures" which is described,
defined, represented, and
calculated by Eq. 26. It is understood that CASM (or any other objects of
mathematical and data objects
this disclosure) can further be processed or go through other mathematical
operations when desired.
It is worth mentioning again (as mentioned before or in the incorporated
references), that all the data
objects of present disclosure and the corresponding matrices vectors etc. can
be made to become
normalized. That is for instance, any desired matrix of this disclosure can
be, and very frequently
desirable, to become column normalized, or row normalized (i.e. the norm or
the length of each column
or row of the desired matrix is unity). Further the multiplications and/or
products of the matrices,
sometime are element-wise and sometimes are inner products and sometimes are
normalized inner
products of the vectors of the corresponding Hilbert space. For instance
A very important, useful, and quick use of exemplary "association strength
measures" of Eq.
17 -26 and 26.1 is to find the real associates of a word, e.g. a concept or an
entity, from their
pattern of usage in the partitions of textual compositions. Knowing the
associates of words, e.g.
finding out the associated entities to a particular entity of interest, finds
many applications in
the knowledge discovery and information retrieval. In particular, one
application is to quickly
get a glance at the context of that concept or entity or the whole composition
under investigation.
The choice and the evaluation method of the association strength measure is
important for the
desired application. Furthermore, these measures can be directly used as a
database of
semantically associated words or OSs in meaning or semantic. For instance if
the composition
under investigation is the entire (or even a good part of) contents of
Wikipedia, then universal
association of each entity (e.g. a word, concept, noun, etc.) can be
calculated and stored for
many other applications such as in artificial intelligence, information
retrieval, knowledge
discovery and numerous others.
Moreover, from the "association strength measures" one can also obtain and
derive various
other "value significance measures" which poses more of intrinsic type of
significances. For
instance in the application 12/939,112 the asmik.,11 j (e.g. Eq. 20-26) was
used to define and
calculate few exemplary "value significance measures", i.e. vsmik 11, in order
to evaluate the
intrinsic importance, credibility, and importance of OSs of different orders.
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
In practice, for given a OS, e.g. 0Sf, we want to find out the strongest
"associated with"
OS (assume it found out to be the 0S1'). To do that we can use Eq. 21. Also
one can use the
Eq. 22 to find out which OS the given OS, say OSik, is highly "associated to"
(assume it was
found out to be the OSic).
To find out the semantically or functionally related OSs one can use Eq. 26
which is an
important tool for knowledge discovery. For instance this measure can be used
to hunt for the
subject matters that can in fact be highly related, but one cannot find their
relations in the
literature explicitly. The "association strength measure" of Eq. 26, thereby
can point to
interesting and important topics of further investigation or research either
by human researcher
or an intelligent machine.
In the next subsection the rational and definition of yet other types of
instrumental
measures and way of calculating them are given
RELATIONAL ASSOCIATION MEASURES
As mentioned above the association strength values are important for many
applications.
One or more of such applications is to cluster or to find hidden relationships
between the
partitions of the compositions. The asmi,i of the lower order OSs can show the
association
strength of the higher order OSs of the composition thereby to use them for
clustering,
categorization, scoring, ranking and in general filtering and manipulating the
higher order OSs.
Accordingly, in this section we further disclose and explain the concept of
"Relational
Association Strength measure" (RASM). In the general terms, from lower order
"association
strength matrix" we can proceed to calculate association strength of higher
order OSs to a lower
order OS that we call it "Relational Association Strength measure" (RASM)
here.
One exemplary instance of such "Relational Association Strength measure" can
be given
by:
r l
RASM _11 = rasm1 kt j' = Mk )T x ASM i1 = 1,2, . .
M and jk
N (27)
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
1¨>likl 1-411cl =
wherein rasm -1tjjk. or
the RASMJ is the "first type relational association strength
measure" of OSs of order / to OSs of order k, which is a MxN matrix and shows
the degree that
an OS of order / (e.g. the iith sentence of the composition) is associated or
is related to a
particular OS of order k (e.g. to the jkth word of the composition) .
It is noted that ASMO is generally a square asymmetric matrix, whose transpose
is not
equal to itself, and therefore there could be envisioned another, also
important, type of
"relational association strength measure". Accordingly, in the same manner the
"second type
relational association strength measure" can be defined and calculated as:
RASM_21-qclk1 = rasm -2. = (pwa)T Asmo.,µ , 1,2,
M and jk =
1,2, ... N (28).
/->tikt
wherein rasm -2. or the RASM 21-91cIla is the "second type relational
association strength
measure" of OSs of order I to OSs of order k, which is also a MxN matrix and
is similar to
RASM_11-+kik1except relational emphasis is from different aspect. For instance
if the ASM used
in Eq. 28 is from the Eq. 20, then for a given OS of order k (e.g. a
particular keyword) the
RASM_11-4kIk1 shows a high relatedness for those partitions (e.g. sentences or
paragraphs etc.)
that contain the words that are highly bonded to the target OS. Whereas at the
same condition
using the RASM_21-)k lki then those sentences that contain the words that the
target OS is highly
associated with show a strong relatedness to the target OS.
Therefore using the above relational rasm one can conveniently find the most
related
partitions of a composition to one or more target OS for the desired goal of
the investigation
(e.g quick retrieval of documents, sentences, or paragraphs with high semantic
relevancy).
On the other way, the RASM_2I-4k lki or RASM_11-4kIki can be used also to find
out the
association strength or relatedness of particular OS of order k (e.g. the jk
th word of the
composition) to a particular OS of order / (e.g. the ilth sentence of the
composition) by having
the following relationship:
RASM_xk-'11k1 = (RASM_xi-+k lia)T
(29).
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
The reason that the present invention call RASM_x1-411c1 "Relational
Association Strength
Measure" of type x, is to remind the fact that these types of association
strength are not only
between a higher order OS (e.g. a sentence, paragraph, or a document, or a
segment/partitions
of a picture) with a lower order OS (e.g. a word or a keyword, phrase, a
pixel, or section of a
picture etc) but it is, in an indirect way, also between a higher order OS and
the associations of
a lower order OS. The name for the other way around relationship (i.e.
RASM_xit-411cl) is also
appropriate in which not only a lower order OS is associated with a higher
order OS but also is
related to other constituent lower order OSs of the higher order OS.
Many more useful mathematical objects and relations are obtained, in a similar
fashion as
thought in the present invention, from which variety of operations can be
envisioned. For
instance we can proceed to calculate the association strength between the OSs
of order / (e.g.
an association strength measure between sentences of a textual composition) by
the following:
RASM_xkt = rasm -x1,-)11k1 =
RASM_x1-41k1 x RASM xic-311k1
= 1,2, .. M
Ltii
(30)
wherein rasm x. is
indicative of one type of "relational association strength measure"
Dc
between ith OS of order / and jth OS of order /. This matrix is particularly
useful to find or select
the higher order OSs of the composition or the partitions (e.g. sentences or
paragraphs, or
documents), that are highly associated with each other. In some applications,
though, it would
be desirable, for instance, to find out the partitions that have the least
amount of associations
with any other partitions etc.
In general one or more of these "related associations measures" can be used
(either
normalized or not) to define and/or synthesize new RASMs.
By the same manner using "Participation Matrix/es" and other objects, other
desired
features can be quantified in a composition or a BOK and consequently make it
possible to
select, clustered, or filter out the desired part or parts of the composition
to look into, investigate,
modified, re-composed, etc.
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
Eqs. 27-30 make it easy to find the partitions of the compositions that have
the highest
relatedness or highest relative association with a keyword or the other way
around etc. Therefore
a computer implemented method utilizing these formulations can essentially
filters out the most
related parts or partitions of a composition in relation to a target keyword.
One immediate application, of course, is for scoring the relatedness of group
of documents
to a subject matter or a keyword. Another immediate application of the
computer implemented
method, utilizing the concept of RASM_xl-)kiki and the formulation, for
instance, is to cluster
and separate partitions of a BOK or a large corpus/s, etc into sets of
partitions that are related to
a particular subject matter. The relatedness is measured by one or more of the
above measures
and partitions that exhibited an association strength value greater (or
sometimes smaller) than
a predetermined threshold to a particular OS, can be grouped or clustered
together. Further
these data can be readily used to build a neural network type system (for
learning, reasoning
etc.) whose edge/connection weights can be obtained from the data of
association strengths of
the ontological subjects (e.g. the node of a neural net). In this way the
training of a neural net
can be done much faster or simply by reading a body of knowledge to attain the
necessary data
for building a learnt (e.g. adjusted weight by training through observing
output/input as done
currently without the teachings of the this disclosure) neural net. The
association strength data
structures usually in the form a matrix therefore is instrumental to build
such cognitive networks
for variety of tasks in general and for building neural nets in particular.
The training iteration
and the resource needed to train a neural net is significantly reduced using
the information of
the association strengths (and various other data objects or data structures
introduced in this
disclosure) of the ontological subjects obtained by investigating a body of
knowledge as taught
through this disclosure.
In light of the foregoing explanation, the algorithm and method of clustering
become
straightforward. For instance, a number of partitions of the composition or
the BOK that have
exhibited a predetermined threshold of relative association strength or
predetermined criteria of
satisfying enough association strength to a target subject or to each other
can be categorized or
being clustered as group together.
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
As a practical example, these method/s, were successfully and effectively used
for
clustering and categorizing a large of number of news feeds as shown in FIG 11
which will be
explained in the next subsections (section
Nevertheless in the short note here, the FIG 11 shows the procedure in which
using the concept
of "value significance" selected a number of head category are selected from
those OSs
exhibiting the highest value significances, and consequently using the
"related association
strength measure" concept it was possible to separate the very many different
news feeds into
different categories automatically with satisfactory accuracy.
In the next section, in accordance with another aspect of this disclosure the
relative or
"relational value significance measures" (RVSM) are further introduced to
evaluated the relative
significances of various OSs in relation to a target OS in the context of the
given BOK.
II-IV RELATIONAL VALUE SIGNIFICANCE MEASURES
Considering the case wherein one is looking for an important partition of the
BOK related
to a target OS (e.g. 0Sf ) which could be a word or a phrase, subject matter,
keyword etc.
Consequently one needs a value significance measure/s that is measured in
relation or relative
to one or more target OS. One can call this conceptual measure as "relational
value significance
measure" or RVSM.
In here the RVSM can simply be the association strengths of OS, i = 1,2, .. N
to a target
OS!' i.e. asmk. IIJk ; or the jk th column of the ASMkli matrix, which when is
used as a VSM
1-,
vector that can give a weighted importance of partitions of the composition or
the BOK (i.e. an
04) in relation to the target Sit when operates (multiply) on the
participation matrix PMk1 ,
as the following:
,cjk ->k kt I41 T k
rvsm_l_x = (pm,0 X asm_yik1_,I ik ..ik,jk = 1,2, ... N and Ii =
1,2, ... M and x,y = 1,2, ..
(31)
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
wherein rvsm_1 -x. .
stands for type 1 of number x "relational value significance measure"
Luk
of OSs of order 1, OSII, to a given OSikk which is a row vector and is
obtained by processing the
participation data of OSk in OS1 or in other words it has been driven from the
data of PMk1
and y is indicative the type of the "association strength measure".
For the sake of simplicity usually the x and y are the same type. Accordingly,
as can be
iti-; :1 kl
seen in this embodiment the first type "relational value significance
measure", rvsm_1
is in fact the same as rasm -11.-)klkithe "first type relational Association
strength measure"
icik
introduced in Eq. 27.
Eq. 31, once executed, will assign values to 0S1 in which it amplifies the
importance or
significance values of the partitions (e.g. sentences) of the composition that
contains the OSs
(e.g. words) that have the highest association strength to the target OSik
(i.e. a target keyword)
thereby to provide an instrument, i.e. a filtering function, for scoring and
consequently selecting
one or more highly related partitions to an OSik.
In fact the Eq. 31 can also be written in a matrix form wherein the rvsm is
a m by N
matrix indicating the relative importance of the partitions to each of 0Si'.
In other words
1k1
rvsm . is a
kind of "relational value significance measure" and can be used as, say,
"first
4
type relational value significance measure" (e.g. can be shown by RVSM_1
notation).
The RVSAL1 therefore, following the Eqs. 27 and 31, can be given in the matrix
form as:
RVSM_1_x1-4k11'i
= RASM_11-41k1 = rvsm -1c.-yclki (pmki)T x Asmkil
tok
1,2,..M and jk = 1,2, N (32)
wherein the "T' shows the transposition matrix operation and RASM_11-41k1
is the
"Relational Association Strength Matrix" and the RVSM_1 is the "first type
relational value
significance measure". It is noticed that ASMk I / is a NxN matrix and RASM_11-
41k1 is a M x
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
N matrix indicating the relatedness/association of 0.51 (e.g. a sentence and
i= 1...M) to a OSik
(e.g. a word and j=1.../V).
In a similar fashion there could be defined a second type relative value
significance
measure (e.g. can be shown by RVSM_2 notation).
as:
RVSM_21->kik1
= rvsnoiti-4ikkiki (p m)T kINx (ASMk11)T it = 1,2,.. M and ik =
...N
(33)
Or equivalently (see Eq. 28) given by:
RVSM 2/-qclk/ = RASM 2/-)Iciki
(34)
wherein the RVSM_21-)kiklor the RASM_21-41k1 indicates the
relatedness/association strength
of OS 1 (e.g. a sentence and i=1...M) or its "relational value significance"
to a 05jk (e.g. a word
and j=1...N).
Remembering the ASMkil in general is asymmetric and have different
interpretation in
which the rows of ASMkil indicates the value of association to other and
column indicates the
value of being association with by others. Therefore the RVSM_11-4c I klis
indicative of a degree
that an OS of order 1, QS/, (e.g. sentences) containing the OSs of order k,
OSk (e.g. the words)
that are used to explain or express or provide information regarding the
target OSik (i.e.
containing the words that are highly associated with the target OS). Whereas
the RVSM_21-41k1
is indicative of a degree that an OS 1 (e.g sentences) containing the OSk
(e.g. the words) for
which the target OS t is used or participated to explain or express or provide
information about
them (i.e. containing the words that the target OS is highly associated with).
Yet a third type of "relational value significance measure" can be defined as:
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
RVSM 3Ikl = vsmk.k 11. RASM 11-)Icild = vsmk.11.((PM1c1)T x ) =
11:1k .1 k
1,2, .. M and jk = 1,2, N (35)
wherein "." indicates an element-wise multiplication and the vsmik.kli could
be the value of the
one of the "value significance measures".
And yet "forth type relational value significance measure" can be defined and
calculated
as:
RVSM - 41.-4c1kt VSMiHl.
RASM 2lIc/ --= vsmk. II. ( (PMki)T x ASMkli ), =
'Ilk k k
1,2,..M and jk = 1,2, ...N (36)
Therefore there could also be defined various "relational value significance
measures" by
incorporating the "intrinsic value significances" and the "relational
association strength".
Accordingly, in general the RVSM_x kk I kican be rewritten as:
1--qc1k1 k11 1k1->klk1 )
RVSM _X = fx(VSnlik , RASM_11k ' RASM_21
(37)
wherein RVSM- is
the "type x relational value significance measure" and the fx is a
predetermined function.
1-4cl/a
These measures, RVSM - 3. ;k and/or RVSM 4/llik put
an intrinsically high value on
the significance of the partitions that are highly related to the high value
significance OSk of
the composition by taking the intrinsic value of the target OSs into account.
Therefore these
measures can be instrumental to, for example, representing a body of knowledge
with the
highest relational value significance or to summarize a composition. To do so
one can simply
select one or more partition of the BOK that scored the highest for these
measures in order to
present it as summary of a composition.
Furthermore, from RVSM_417kkik1 one can proceed to calculate the "relational
value
significance measures" between the OSs of higher order / as:
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
RVSM_x1-4111c1 rvsm_x = RVSM_xl-qclkl x (RVSM_xl) T =
(38)
wherein RVSM_xlki is the relative value significance measure between OSs of
order / so that
it can directly measure the relatedness of partitions of the BOK such as
sentences, paragraphs,
or documents to each other. Again this measure therefore can readily be used
to find the highly
related partitions of the BOK either for retrieval purposes, rankings,
document comparisons,
question answering, conversation, or clustering and the like.
The concept behind the "relational value significance measures" is for
processing and
investigating compositions of ontological subject as it become important in
these investigations
to have tools, measures, and filtering functions and methods of building such
filtering functions
to spot a partition relevant to another part or partition or to a given
composition or query.
For instance in the information retrieval it becomes increasingly important to
have
retrieved the most relevant pieces of information and therefore the retrieved
documents or the
parts thereof should be the most relevant document and partition to a target
OS which could be
a keyword or set of keywords or even a composition itself. For instance it
would be very useful
and desirable to find the most relevant document or piece of knowledge to an
input query in the
form of a natural language question, or even a paragraphs or a whole text
document. In this
particular application one or more of the various kind and types of the, so
far introduced, "value
significance measures" can readily be applied using the method of this
disclosuer to retrieve
and present the most relevant part (e.g. a word, a sentence, a paragraph, a
chapter, a document)
to the sought after subject matter or in response to a query.
Many other desirable outcome and functionality can be built in light of the
teachings and
the disclosed method of systematic and computer-implementable methods of
investigations not
only for textual compositions but also for other types of compositions. In
fact the disclosed
method has been used and applied on image and video compositions as well as
genetic code
compositions which confirmed the method/s is indeed very effective in
investigating
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
compositions of ontological subject to obtain a desirable outcome or
information or knowledge
or the result..
In another aspect of the present invention, in the next section, are the
concept and
definitions of "novelty value significance measures" (NVSM), as indication of
various situations
of novelty of OSs in the composition or the BOK.
II-V-NOVELTY VALUE SIGNIFICANCE MEAUSRES
According to another aspect of investigation methods of compositions yet other
value
significance measures are introduced and explored herein. According to this
aspect of
investigation, in some instances it would become desirable to have found the
words or the
partitions of a composition expressing novel information about one or more
subject matter/s. In
these instances if one can have an instrument or a function to measure a
novelty value of a
subject matter (e.g. an OS of the composition) itself or a novelty measure for
the partitions then
it would become practical to spot the novel information and/or the partitions
of the composition
carrying novel information in the context of that compositions or a set of
compositions or
generally a body of knowledge (BOK) as we defined before.
However the degree or value of novelty should be somehow measured in order to
identify
the part or partitions of the novelty and evaluate their value in terms of the
significance of their
novelty. In this disclosure these measures are called "novelty value
significance measures"
(NVSM) which can be categorized in different types and we, herein, define and
show the
methods of evaluating them for ontological subjects of a composition or a BOK.
In view of that, the first step is to define what constitute a novelty in the
context of a BOK
and identify different aspects that there is into a novelty investigation.
There could be envisioned several situations in which a novelty can occur that
is of value
in the investigation process. The detection and evaluation of novelty values
can be important to
either a knowledge consumer or to be used in other applications, processes,
and or other
computer implemented client programs.
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of ontological subjects and intelligent systems therefrom".
Accordingly, in the present invention we explain few exemplary instances of
novelty,
having significance value, to be investigated in more details to demonstrate
another
investigation method of compositions according to novelty significance
aspect/s.
II-V-I RELATIONAL NOVELTY:
Novelty is an attribute that is related to newness, surprising factors,
entropy, not being well
known, not seen before, and unpredictability. However this attributes depends
very much on
the context and in relations to other ontological subjects of the
compositions. For instance
something which is new in one domain or context might be an obvious thing in
another domain.
Or something that is new now, it might become very well known fact after
sometimes. For
instance, in news aggregation novelty of the news is very much related to the
time of the news
being broken and how many other news agencies have published the same news
story. Therefore
the novelty should be measured in relation to the context, time, and other
partitions of the
compositions. However, we look for novelty or novelties in the given
composition for
investigation and since we can treat time and/or a time stamp as an OS, our
method of
investigation, therefore, would also work for time-related compositions such
as news, as well.
Generally, therefore, a valuable novelty occurrence is relational (i.e. more
than one OS is
participated where the novelty occurs) which should be investigated in the
context of a
composition. For instance in the context of a body of knowledge (BOK) there
could be found
many known or anticipated facts in regards to the subject matter/s of the BOK
but there could
be some partitions, e.g. statements, that are less known and can be considered
as novel.
In this subsection therefore, to identify relative or relational novelty in
regards to a topic or
one or more OSs, several important novelty occurrence situations are
envisioned and
exemplified in the followings.
One of the situations is a novel relationship between two or more OSs in which
case there
could yet be envisioned at least two notable and important situations.
In one situation of novel relationship between two or more OSs, for example, a
type of
"relational novelty value significance measure" can be assigned to spot a
novel or less known
relationship between two important OSs. In this case the relational novel
value should be high
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of ontological subjects and intelligent systems therefrom".
because the two significant OSs are less seen with each other in a part or
partitions of a
composition or a BOK. Therefore the desired "relational novel significance
measure" should be
proportional to the value significances of each of the OSs and be inversely
proportional to their
"association strength bond".
Accordingly, one exemplary and simple measure of "relational novel value
significance"
between two of the OS of order k, say OS t and 0,51`, can be given by:
rnvsm 1. (0,S!`,O.S!`) a vsmtk.11,vsmk.li kil (39)
- j j
conlij
kit
wherein the rnvsm-1i_1stands for type one "relational novelty value
significance measure" of
OS t to the OSic. This measure can be used to hunt for those partitions that
contain two or more
significant OSs expressing less known relationship. Therefore this measure
will give a high
value to the pair of the OSs, that are intrinsically significant, and more
likely the expressed
relationship to be credible and significant yet their relationship with each
other is of novelty in
the context of the BOK.
Another situation of novel relationship between two or more OSs, is a type of
novelty between
two OSs in which the novelty reveals less known information about one
important OS of the
interest (e.g. a target keyword, a high value significance subject of a BOK,
etc.), regardless the
significance of the other OSs. In this instance, the intrinsic value of the
target OS, e.g. an
intrinsic vsm, should be a significance factor for measuring and putting a
value on the novelty.
Also in terms of how to spot a novelty in relation to a significant target OS
then the less known
associations can be a guide to find the novel part or partitions or statement
of a relationship
between a significant OS with other OSs of the composition.
Therefore, another type of "relational novelty value significance measure" can
be defined
as:
kli I le
rnvsm 2. OS:-, 0Sql oc vsmk.11 (40)
- j = kii
comii
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
wherein the rnvsm -2k I/ stand for the second type "relational novelty value
significance
measure" OS t to the OSic. This measure put a high relational novelty value on
the pairs that at
least one of them, e.g. the target OS, have a high intrinsic value (i.e the
vsm of the OSP) while
the other ones are the ones that had the lowest co-occurrences with the target
OS. This measure
can be used to spot the partitions that are novel and significant but perhaps
the expressed
relationship, between the two OSs, by the partition, is less credible.
Moreover there could be considered further notable situations, when two or
more of OSs
of the composition have participated in a partition, to convey a novel
knowledge or information.
Accordingly, for example, another type of relational novelty can occur between
a less
significant OS and a high significance target OS. In this case this type of
novelty value should
be proportional to the value significance of the second OS, e.g. a target OS,
and be inversely
proportional to the value significance of the less significant OS and also be
inversely
proportional to their co-occurrences so that:
rnvsm - 31`.11 (OS?1' ,OSJ') oc vsmk.11,1/vsmkil kl (41)
i
CON
kil
wherein the rnvsm_31 stand for the third type of "relational novelty value
significance
measure" OS/' to the 0S1`. This measure can be used to spot highly novel but
perhaps even less
credible partitions of the BOK than what is found by the rnvsm_2ik,Il j.
And yet another type of novelty can occur between two less significant OSs. In
this case
the significance and relational novelty value should be inversely proportional
to the
significances, i.e. VSMs, of each of the OSs and also proportional to their co-
occurrences so
that:
kit
rnvsm - 4j. (O.5!`A OS!') 1/vsmk.11,1/vsmk. 11, comk..II (42)
I J 1 IJ
kil
wherein the rnvsm_4./ stands for the forth type of "relational novelty value
significance
measure" OS t to the OSI'. This measure can be used to spot a highly novel
relationship between
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of ontological subjects and intelligent systems therefrom".
two less known OSs but with some credibility. This measure can be used to spot
the rare
partitions that might be irrelevant to the context of the BOK but is important
to be looked at.
And yet there could be another notable situation and measure of relational
novelty as:
kit
rnvsm 5. (OS!', OS!') cc 1/vsmk.11, 1/vsmikli, __ 1 kii (43)
- / comij
111
wherein the rnvsm -5 . stands for the fifth type of "relational novelty value
significance
measure" OS' to the 051'. This measure can be used to spot a highly novel
relationship between
two less known OSs but with even less credibility than rnvsm_4ik,111. This
measure can be used
to spot the noise like partitions that might be irrelevant to the context of
the BOK but might be
essential to be looked at such as crime investigation or financial analysis,
fraud detections and
the like. This measure also can be used to filter out the irrelevant or noisy
part of the
composition, or be used in data compression, image compression and the like.
In another notable instance a measure of relational novelty value can be
defined based on
their association strengths to each other as:
kil
rnvsm-6(OSIC, OSJ!') cc asmk.111./asmk.11. (44)
1,
kit
wherein the rnvsm -6. stands for the sixth type of "relational novelty value
significance
measure" OSt to the OS]'. This measure of novelty amplifies the asymmetry of
the association
strength value between the two OSs and therefore serves as a measure of
anomaly and novelty,
both too large and too small a value for this measure can point to a novelty
situation. However,
to have a symmetric mvsm using asm one might consider the following measure:
kit kit
kit k asmi,i
rnvsm 7. OS:- 05q9 cc ( _________________________________________ (45)
- + kit )
asmi_,i asmi,i
wherein the rnvsm - 7k. 11 stands for the seventh type of "relational novelty
value significance
measure" OS,' to the OS!' This measure is particularly good to spot any
symmetric kind of
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of ontological subjects and intelligent systems therefrom".
novelty or anomaly between OS 1 to the 0Sf. When the value of this measure is
large then there
is a novelty situation to look at between OS t to the OSP.
It can be noted that the some of the exemplary rnvsm_x, (x=1,2,3..) are
generally
symmetric and both sided whereas the some other rnvsm_xik. are asymmetric.
Once is noted that the co-occurrence is one of the measures and indications of
the
associations between a pair of OS then the rnvsm_xkll (x=1, 2, ..) can further
be generalized as
a function of individual values significances of the OSs and their association
strength measures.
Therefore in general the "relational novel value significance measures" can be
defined and
calculated in the general form of:
rnvsm- II (OS!' J,OS') "1" V' I" tt ni
asm)
kil, ===i j = 1,2, ..N,x =
1 ) t,
1,2, ... (46)
wherein 92 is a predefined or predetermined function.
When there are multiple OSs of interest the pair-wise value significances can
be used in
combination and perhaps with various weight to achieve the same filtering
effect for a set of
OSs. For instance
rnvsmj (OS!' , OS!' OSk) = ai.rnvsm xlk11(0Sk OS!') +
I p
a2. rnvsm_x2k11(04,05n a3. rnvsm_x3k11(0Sqk, 0 Sip) and q = 1,2 N
(47)
wherein al, a2, and a3 are predetermined weighting functions such as al(OSik)
= 1/FO(OS)
or a1(0S1) = 1og2(iop(OSI)) etc. or constants and/or normalization factors,
and xl, x2 and
x3 are indications of the type of the rnvsm (e.g. Eq. 39-45) and "04" is the
indication of one
or more combination of the first OS to the particular target OS. Moreover, Eq.
47 in just one of
the notable situations of novelty occurrence and in another instance it might
become more useful
to multiply the pair-wise rnvsm_xk I / to each other.
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of ontological subjects and intelligent systems therefrom".
All these relationships (i.e. Eq. 39-46) can be written in a matrix form to,
once executed
numerically, have all combinations of relations between two or more of the OSk
pre-calculated
and handy.
Again by operating these specialty defined "value significance measures" on
the PM one
can obtain the respective type of value for the partitions of the
compositions, e.g. OSs of order
/ or OS1 ,by:
1-->xik/ kl T kil
rnvsm_x/i x , jk = (pmikii) rnvsm_xik,ik . . ik, jk =
1,2, ... N and i/ = 1,2, ... M
(48)
Or in the matrix form as:
RNVSM_x'1' = (pwa)T x RNVSM_xic II 11= 1,2, ... M and jk = 1,2, ... N
(49)
1¨>
wherein the "T' shows the transposition matrix operation and the RNVSM_xklicl
is the type
x (x=1,2,...) "relational novelty value significance measure" of the
partitions or OSs of order 1
to the OSs of the order k. It is noticed that RNVSM_x1-) is
aMxN matrix indicating the
type x (x=1,2,...) "relative novel value significance measure" of 0S1 (e.g. a
sentence and
1,2,...M) to a 0S1' (e.g. a word and j=1,2,.../V) and RNVSM_xkll is a NxN
matrix indicating
the type x (x=1,2,...) "relational novel value significance measure" of OSk
with OSk
In a similar fashion to the previous subsection, there could be calculated a
novelty type
relationships between the OSs of order 1 so that to show how each pair of the
partitions are
related in terms of the significance of the relational novelty to each other
as:
RNVSM_xi-qua RNVSM_xl->kikl x RNVSM_xk->liki (50)
wherein RNVSM_xlkl stands for the "relational novelty value significance
measure" of type
x between the OSs of the order 1, which is aMxM matrix. This measure and the
data of such
matrix can be used to find a novel partition, exhibiting a predetermined range
of "relational
novelty value", for a given partition. Also these measures can be combined
with other measures
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
to obtain the desired parts of the compositions that one is looking for (e.g.
in response to a query
or a question).
II-V-II THE ASSOCIATION TYPE NOVELTY:
Many associations are hidden that when is revealed is obviously a case of
novelty existence
or occurrence. For instance when two OSs have little direct associations but
their association
spectrum is highly correlated then there could be a novelty of high value
revealed for further
investigation. In these instances a measure to hunt for these types of novelty
association can be
given by:
kit kit
kil (aSin_Xlp,i. asm_x2p,i)
anvsm -1. : S`) a __________ kit p = 1,2, N
OSIk, 0J
asm_x3
(51)
wherein anvsmikil is indicative of the first type "association novelty value
significance
measure", the "." shows the inner product or scalar multiplication of the
asm_x1pk,iti and
asm x2kl/ vectors. The indices of x1, x2, x3 (=1,2,..etc) are usually equal
and can refer, for
instance, to the first or the second type association strength measure (given
by Eq. 16, and/or
17-26).
This measure of novelty gives a high value to the relational novelty of those
pairs that
exhibit strong hidden association correlation but they are not explicitly
strongly bonded. This
measure is particularly useful for detecting hidden relationships between two
OSs of interest,
i.e. OSik and OSt and can be used to spot the cases worthy of further research
and investigation
(e.g. in scientific discovery, medical, crime investigation, genetics, market
research and
financial analysis etc.).
Although anvsm_lkil is also one of the "relational novelty value significance
measures"
but in here it is preferred to be given a more distinct name as "association
novelty value
significance measure" (ANVSM) in order to have a distinct category for this
kind of "value
significance measure" in general.
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of ontological subjects and intelligent systems therefrom".
To further amplify the significance of the novelty of anvsm_1k1t one can
further
incorporate the intrinsic value significance of one or both of the value
significances of the OSt
and OS.7 as, for example, the following:
kit
(VSM_ylikit vsm_y2kit ) x (asm_xl. asm_x2kitp,1)
anvsm ¨ 2k. 114 (OS!' 051 oc ______________________________________
/¨
asm -x3111
p = 1,2, ... N
(52)
wherein y1 and y2 indicates the types and numbers of the "value significance
measure"
used in this formula.
The proportionality factor can be adjusted to account for normalization of the
vectors when
desired.
Eq. 51 can be re written in matrix form in general terms which is more useful
as:
ANVSM_1kli = R ASM_x1k1i)T x ASM_x2k1/ 1./ ASM_x3k11
(53)
wherein "x" shows the matrix multiplication operator and ".1" shows the
element-wise
division. Usually, in the preferred exemplary embodiment, in the Eq. 53 the
ASM_xkil are
column or row normalized.
As can be seen Eq. 51, 52 and 53 are generally the exemplary cases of the
general form of:
anvsm_xik,11 j (0 , OSIO
kit kit kii kit
g3 (vsm_yli .vsm_y2jk.11, asm_xl. asm_x2p,1, asm_x3
asm_x4i,1), ...p, i,j =
(54)
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of ontological subjects and intelligent systems therefrom".
wherein g3 is predetermined or predefined function and y1, y2, x1... x4 etc
refer to the selected
type of the respective kind and type of the "value significance measure".
Numerous other forms of "value significance measures" using one or more of the
introduced "value significance measures" and the concept behind them can be
devised, depends
on the applications, which are not further listed here, and in light of the
teachings of the present
invention become obvious to those skilled in the art.
II-V-III THE INTRINSIC NOVELTY
Another important situation of novelty occurrence would be to spot and find
the novel OSs
and the partitions of the composition regardless of their relationship and
just for being
intrinsically novel in the context of the composition or convey novelty
wherever they appear in
the composition or the BOK.
In this case we assign an intrinsic "novelty value significance measure"
(NVSM) to each
desired OS and then use the NVSM to weight the intrinsic novelty value of
other partitions.
The first measure of novelty of course can be derived and defined based on the
independent
probability of occurrence so that:
nvsm_1k1ll = hi(iopikli), i = 1,2, ... N (55)
wherein h1 is a predetermined function such as hi (x) be a liner function
(e.g. ax+b), power
of x (e.g. x3 or x 33), logarithmic (e.g. allog2(x)), 1/x, etc wherein a or b
might be scalar
constant or a vector.
Usually the term "novelty" implies that it should be inversely proportional to
the popularity
or frequency of occurrence or independent probability of occurrence and
therefore nvsm_likli
is usually more justified when the choice of h1 is such that it decreases as
the iopi increases.
For instance one good candidate for defining and calculating a "novelty value
significance
measure" as a vector is:
. _
nvsm 11. = cp. , I ¨ 1,2, N
- - (56)
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of ontological subjects and intelligent systems therefrom".
wherein c might be a scalar or a constant vector. In another instance it might
be defined as :
kIt kit
nvsm_1_21 = c / log b (iopi ), i = 1,2, ... N (57)
or in another instance:
nvsm_1_3,1511 = C. log b(1/ = ¨c. log b(iop kit licit), i = 1,2,
... N (58)
or yet in another instance:
kIl log b(iop tk.11)
nvsm_1_41 = c. _________ kii (59)
iopi
wherein b is a constant and c could be constant or a vector. For example c can
be an auxiliary
vector that when multiplies to other vectors it suppresses or dampen the value
of particular OSs
of the compositions such as the generic words in a textual composition.
Accordingly, by the same manner, there could be defined various "novel value
significance
measures" if the justification is properly done. For instance with combination
of one or more
of the nvsm_x or other variables there could be defined more sensible and
useful novelty
value significances. As can be seen in Eq. 59 the nvsm_1_4jkli is in fact
obtained by
multiplication of the nvsm_1_11kit and nvsm_1_31kit.
In another aspect the novelty is observed in relation or combination with
other OSs since
novelty could occurs in a context and therefore in relation to other
ontological subjects. The
stand alone or the intrinsic "novelty value significance value" in this case
is defined as sum of
the novelty that an OS will have with a desired number of other OSs.
These measures of novelty are intrinsic since it adds up all the pair-wise
novelty values for
each OS" so that a NVSM type 2 can be defined as:
NVSM_2k11(0St) = c E = rnvsm ¨ xlc.11.(0Sqc J
,OS!') (60)
t¨> t
wherein the pair-wise novelty measures are summed over the column (i.e. the j
subscript).
Similarly another type of intrinsic novelty value significance measure can be
defined as:
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of ontological subjects and intelligent systems therefrom".
NVSM_3k11(05.1.c) = c Ei rnvsm_xik.,111(OSik, OSt) (61)
wherein the summation is over the rows (i.e. the i subscript).
The same can be calculated using anvsm_xIii as:
NVSM_4k I / (0St) = cEjanvsm_x(OSt,OS11 (62)
and also:
NVSM_5k II (0.54.c) = c Ei anvsm_xik,11j(051,0Sf) (63).
Or in a general form any combination of them can still serve as an intrinsic
measure of novelty of
the OSs of the composition as:
NVSM_xkll (0Sik) = h(NVSM_1111, NVSM_2kli, NVSM_ykli), (64)
wherein h is predetermined function and y is the type and number of the
particular NVSMkli
used into building other types of NVSM_xkli.
These various novelty value measures can find and have many applications in
variety of
applications and compositions which can be employed to investigate such
composition to find
and investigate the parts or partitions of novelty values. For instance they
can be employed for
textual composition processing such as question answering, summarization,
knowledge
discovery, as well as other kind of compositions like detecting novel and
valuable parts in a
genetic code strings, finding and filtering the junk DNA, as well as other
compositions such as
image and video compositions and signal processing such as edge detection,
compression,
deformations, re-composition to name a few.
II-VI-TRANSFORMATION AND ALTERATION OF DATA OBJECTS:
The parameters, vectors, and matrices of the present invention are
transformation of the
information hidden in the participation matrix which can be used for different
applications with
ease, convenience and efficiency to investigate various aspects of interests
in the BOK such as
extracting the most significant parts or partitions, finding the highly
associated concepts or parts
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of ontological subjects and intelligent systems therefrom".
and partition, finding the novel part/s or partition/s of the BOK, finding the
best piece of
informative part of the composition, clustering and categorization of the
partitions of the
composition or the BOK, ranking and scoring partitions of a composition based
on their
relatedness to a subject matter (e.g. a query), excluding one or more
partitions or OSs of the
BOK or suppressing their role in the analysis, and numerous other application.
Moreover the mathematical objects and data arrays can be easily transformed to
other
forms, filtered out the desired part or segment of a matrix, amplify or
suppress the role of one
or more of the OSs of the composition and/or their values being altered
numerically without
needing to manipulate the input composition string or file. For instance in
many of the above
calculations it will be more useful to have the matrices or vectors being
normalized in order to
make the comparisons more meaningful in the context of the BOK. Accordingly
one or more of
such mathematical objects and data arrays (vectors, matrices etc.) can and
might be desired to
become column or row normalized or further being multiplied by other matrices
or vectors as a
mask or filter etc.
Moreover all these matrices (e.g. such as PM, COM, ASMIs, RASM, RVSMs NVSM,
RNVSMs etc.) can be regarded as an adjacency matrix for a corresponding graph
wherein the
matrix carry the data of the connectivity between the nodes or objects of the
graph. Therefore,
from these connectivity matrices one can proceed to calculate a corresponding
eigenvalue
equation/s in order to estimate and calculate other types of desirable value
significance measure
or in general any type of value significance. These measures of value
calculated from the
corresponding eigenvalue equations of the matrices are generally indication of
intrinsic
significance values of the OSs. For instance in the non-provisional US patent
applications of
12/547,879, 12/755,415 and 12/939,112 one or more of these matrices have been
used to
calculate the significance values of the OSs of the composition based on their
centralities of the
corresponding node in the graph that could be represented by that matrix. The
centrality value
can be, for instance, be the values of largest eigen vector of the eigen value
as described in the
applications 12/547,879, 12/755,415 and 12/939,112 which are incorporated here
as references.
II-VI-I-SPECIAL CASE COVEYERS:
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In many cases one wants to deliberately amplify and/or dampen or suppress one
or more
of the values of OS of the BOK in order to achieve the right functionality out
of the analysis and
investigation. Therefore there could be per-built or pre-determined VSM values
(e.g vectors)
that can be used when it is desired to alter and influence the significance
values of one or more
of the OSs of the compositions. For instance these vectors or filter can be
designed in such a
way to amplify the significances of proper sentences of compositions written
in a particular
natural language such as English. For example, in another instance, the
objective can be to give
significance to particular types of partitions of the composition having of
particular feature/s,
attribute/s, or form/s. For instance when one like to hunt the partitions
containing connecting or
the concluding remarks then one may construct a vector that assigns a low
significance value
to every OS except those selected OS (e.g. words or phrases such as
"therefore", "as a result",
"hence", "consequently", "so that"... etc.). n another instance, one might
have list of OSs that it
is not desirable to participate in the calculation (e.g. stop words) one can
provide a vector over
the range of OSs having a value of one expect for those selected OS that must
be omitted from
the calculation.
These pre-assigned vectors are called "special cases conveyers" herein or
"significance
value conveyer vectors" as shown in FIG 6c, that can be used solely or in
combinations with
other VSM value vectors to obtain the desired functionality from the
investigation. These
conveyers are assigned and used based upon the goal of investigation. The
special conveyers
can be designed and altered for various stage of the process and can be used
in different stages
of calculations and processes.
II-VI-II-PM TRANSFORMATION:
In accordance with another aspect of the methods of investigation of the
compositions of
ontological subject of the present invention, the participation matrix can,
for instance, routinely
being transformed to other types of objects or participation matrices by
operating one or more
vector or matrices on the PM. For example one can multiply the PM by a
diagonal matrix (M
by M) from the right side whose diagonal values are the reciprocal of the
number of constituent
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of ontological subjects and intelligent systems therefrom".
OSs of order k in the partitions or the higher order OS of order 1. The
"resulting PM" matrix
will become a column normalized PM and values of the entries will become the
weighted
participation factor. For instance from a binary PM one can get to partial PM
in which if a word
has participated in a sentence with 5 words then its participation entry in
the PM would be 1/5
and if the same word has participated in a sentence with 10 words its
participation entry would
be 1/10 and so on. In another instance, in a similar situation, it become
desirable to have a
"resulting PM" with column geometrical unitary (i.e. the length of the column
become one), in
this case therefore the elements of the diagonal matrix are the inverse of the
square-root of the
sum of the square of the individual elements of the original respective PM
column (or row).
As another instance of transformation, moreover, the PM matrix can be
multiplied from
the left side by a diagonal matrix (N by N) whose entries are a vector that
will put a value on
the OS of the order k so that their participation weight will be altered. For
instance if the diagonal
of the left matrix is one except for some particular words (such as the
generic words of a natural
language) for which the corresponding entries are suppressed (e.g. replaced
with 0.1) then the
role of those particular words (e.g. the generic words) in the computations
will be suppressed
as well, without having to manipulate the original string of the compositions
in order to achieve
the same goal of suppressing the role of generic words.
As another instance of transformation and alteration, one or more auxiliary
vectors (i.e.
filters) can be built to dampen the significance of particular OSs of the
composition by
multiplying those vectors on the resulting vector objects such as one or more
of the different
types and number of the "value significance measures" vectors or matrices.
Moreover the method/s can conveniently be used for compositions of different
nature such
as data file compositions, e.g. audio or video signals, DNA string
investigation, textual strings
and text files, corporate reports, corporate databases, etc. For instance the
investigation method
disclosed herein can be readily used to investigate image and video files,
such as spotting a
novelty in an image or picture or video, edge detection in an image, feature/s
extraction,
compression of image and video signals, and manipulating the image etc. The
disclosed
methods of the present invention can readily be applied in applications such
as, artificial
intelligence, neural network training and learning, network training, machine
learning,
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of ontological subjects and intelligent systems therefrom".
computer conversation, approximate reasoning, as well as computer vision,
robotic vision,
object tracking etc.
Numerous other forms of "value significance measures" using one or more of the
introduced value significance measures and the concept behind them can be
devised and
synthesized accordingly, depends on the application, that are not further
listed here but in light
of the teachings of the present invention become obvious to those skilled in
the art.
The disclosed frame work along with the algorithms and methods enables the
people in
various disciplines, such as artificial intelligence, robotics, information
retrieval, search engines,
knowledge discovery, genomics and computational genomics, signal and image
processing,
information and data processing, encryption and compression, business
intelligence, decision
support systems, financial analysis, market analysis, public relation
analysis, and generally any
field of science and technology to use the disclosed method/s of the
investigation of the
compositions of ontological subjects and the bodies of knowledge to arrive the
desired form of
information and knowledge desired with ease, efficiency, and accuracy.
Furthermore, as pointed out before, those skilled in the art can store,
process or represent the
information of the data objects of the present application (e.g. list of
ontological subjects of
various order, list of subject matters, participation matrix/ex, association
strength matrix/ex,
and various types of associational, relational, novel,
matrices, co-occurrence matrix,
participation matrices, and other data objects introduced herein) or other
data objects as
introduced and disclosed in the incorporated references (e.g. association
value spectrums,
ontological subject map, ontological subject index, list of authors, and the
like and/or the
functions and their values, association values, counts, co-occurrences of
ontological subjects,
vectors or matrix, list or otherwise, and the like etc.) of the present
invention in/with different or
equivalent data structures, data arrays or forms without any particular
restriction.
For example the PMs, ASMs, OSM or co-occurrences of the ontological subjects
etc. can be
represented by a matrix, sparse matrix, table, database rows, dictionaries and
the like which
can be stored in various forms of data structures. For instance each layer of
the a Pm, ASM,
OSM, RNVSM, NVSM, and the like or the ontological subject index, or knowledge
database/s
can be represented and/or stored in one or more data structures such as one or
more
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dictionaries, one or more cell arrays, one or more row/columns of an SQL
database, one or
more filing systems, one or more lists or lists in lists, hash tables, tuples,
string format, zip
format, sequences, sets, counters, or any combined form of one or more data
structure, or any
other convenient objects of any computer programming languages such as Python,
C, Perl,
Java., JavaScript etc. Such practical implementation strategies can be devised
by various
people in different ways.
The detailed description, herein, therefore describes exemplary way(s) of
implementing the
methods and the system of the present invention, employing the disclosed
concepts. They
should not be interpreted as the only way of formulating the disclosed
concepts, algorithms,
and the introducing mathematical or computer implementable objects, measures,
parameters,
and variables into the corresponding physical apparatuses and systems
comprising
data/information processing devices and/or units, storage device and/or
computer readable
storage media, data input/output devices and/or units, and/or data
communication/network
devices and/or units, etc.
The processing units or data processing devices (e.g. CPUs) must be able to
handle various
collections of data. Therefore the computing units to implement the system
have compound
processing speed equivalent of one thousand million or larger than one
thousand million
instructions per second and a collective memory, or storage devices (e.g.
RAM), that is able to
store large enough chunks of data to enable the system to carry out the task
and decrease the
processing time significantly compared to a single generic personal computer
available at the
time of the present disclosure."
H-VH- THE EXAMPLARY IMPLEMENTATION METHODS AND THE
EXAMPLAY SYSTEMS AND SERVICES
This section describes few exemplary systems that can be constructed in order
to
demonstrate the enabling benefits of the deployment of the disclosed method/s
of investigation
of compositions of ontological subjects in various challenging applications
and important
functionalities.
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
As was described throughout the description the goal of the investigation is
to produce a
useful data, information, and knowledge from a given or accessed
composition/s, according to
at least one aspect of significance or the goal/s of the investigation.
The result of the investigation can be represented in various forms and
presentation style
and various devices of modern information technology (private or public cloud
computing,
wired or wireless connections, etc.). The interaction between a client and an
investigator,
employing one or more of the disclosed algorithms, can be facilitated through
various forms of
data network accessibility to an investigator through various interfaces such
as web interfaces,
or data transferring facilities. The result of the investigation can be
displayed or provided in
various forms such as interactive page/device environment, graphs, reports,
charts, summaries,
maps, interactive navigation maps, email, image, video compositions, voice or
vocal
compositions, different nature composition such as transformation of a textual
composition to
visual or vice versa, encoded data, decoded data, data files, etc.
For instance a goal of investigation can be to finding out the OSs of the
composition scoring
significant enough novelty value in the context of the given BOK or an
assembled BOK wherein
the OSs of the composition can be words, phrases, sentences, paragraphs,
lines, document or
the like for the BOK under investigation.
Another exemplary goal of investigation can be to get a summary of the
credible statements
from a BOK or to modify a part or partitions of a composition (e.g. a
document, an image, a
video clip etc.). Or another instance of investigation can be to obtain a map
of relations between
the most significant parts or partitions of the BOK. For instance a patent
attorney, inventor, or
an examiner can use the disclosed method to plan his/her claim drafting by
investigation the
application disclosure and get the most valuable or novel part of the
disclosure to draft the
claims. Or to get the map of relationships between the components (i.e. the
ontological subjects
) of the disclosure in order to draft a summary, an abstract, an argument, one
or more claims,
litigation, etc. Or the method can be used for examining the application in
comparison to one
or more collection of one or more patent application disclosures.
In another instance an intelligent being (e.g. a software bot/robot a
humanoid, a machine,
or an appliances) can use the system and methods internally or by
connecting/communicating
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
to a provider of such services to become enabled to interact intelligently
with human (e.g.
conversing and doing tasks, or entertaining, or assisting in knowledge
discover etc.). And many
numerous other examples that could be using one or more of the tools, measures
and method/s
given in this disclosure to get information and finding/composing the
knowledge that is being
desired or seek after.
Referring now to the accompanying drawings in here, few exemplary embodiments
of the
methods, the systems and the applications are further illustrated and
explained in order to
demonstrate the deployment of the teaching of the present invention.
Referring to FIG 1 here, it depicts one general flow process and the system
that can provide
one or more exemplary investigation's result, as services, utilizing the
algorithms and the
methods of the present invention. As shown in the diagram, following the above
formulations
and methods of building the required variables or the mathematical or data
objects (e.g. the
matrices and the vectors values etc) and building the various filter, one can
design, synthesize,
and compose an output according to her/his/it's need or goal of investigation
or informational
requirements and for an input composition. For example if one applications
calls for getting the
most credible and valuable partitions of an input compositions then she/he/it
must chose (or
select through an interface) the corresponding filter (i.e. the suitable
XY_VSM/s and
algorithm/s) for which to obtain such a credible glance or summary of the
composition.
Moreover the user or the designer of such system and service can synthesize
the suitable filter,
using the tools, measures and methods of the present invention to provide the
desired response,
output or the service.
Alternatively, in another instance, if one is looking only to get the novel
parts of the input
composition then that can also be readily done following the teaching and
computational
process of the above to get the novel parts or partitions of the composition
using the one or more
of the novelty value significance measures.
Turning to FIG 1 again, as seen in the FIG 1, the input composition is used to
build or
generate the one or more participation matrices while the ontological subjects
of different orders
are grouped, listed, and kept in the short term or more permanent storage
media. The actual OSs
or the partitions usually are used at the end of the processing and
calculations of the desired
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
quantity or quantities, when they are fetched again based on their
corresponding value for one
or more measures of the values introduced in previous sections. Accordingly
after having the
PMIs the system will calculate the desired mathematical objects such as COM,
ASM/s, the
desired VSM/s, one or more RASM if needed for the desired service, one or more
RVSMIs if
needed for the service, one or more of NVSMIs, or RNVSM/s or ANVSMIs if
desired and so on.
These data objects (e.g. matrix/es or vector/s) are used to synthesize the
required filter to
provide the desired functionality once it operated on the PM. After operating
the filter on the
PM, the output is further investigated for selection of suitable OSs of the
composition for further
processing or re-composing or presentation. The output can be presented in
predetermined
form/s or format, such as a file, displaying on a web-interface or an
interactive web-interface,
encoded data in a particular format for using by another system or software
agent, sending by
email, being displayed in a mobile device, projector and the like over a
network, or sent to a
client over the internet and the like.
For instance if the desired mode of operation is to find out the novel
partitions of the
composition exhibiting enough novelty value while having enough significance
then the
corresponding filter will use the RNVSM of the Eq. 39 for finding, scoring and
consequently
selection of the suitable partitions for this requested service.
In another word after the composition data are transformed or transported into
participation
matrix/matrices then we only deal with numerical calculations that will
determine the value of
the members of the listed OSs and (based on their index in the list or based
on their row or
column number in the participation matrix) once the value for the
corresponding measure was
calculated then those OSs that exhibited the desirable value or range of
values are selected by
the selector or a composer that provide the output data or content, e.g. as
service, according to
predetermined formats for that service.
In references to FIG 2 now, it involves the conceptualization of the
association strength
measure/s. As exemplified several times along the disclosure the concept and
values of
"association strength measure/s" plays an important role in investigation of
the composition of
ontological subjects as well as providing the data that is valuable itself.
That is, knowing the
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Patent Application of Hamid Hatanni-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
association strength of OSs to each other is important and can be used to
build many other
applications especially in artificial intelligence applications.
Accordingly, in FIG 2 here, it is shown one general form of conceptualizing
and defining
the association strength measures and consequently calculating the association
strength values
for those measures. As seen in this exemplary embodiment the association
strength of the OSs
of order k that have co-occurred in one or more OSs of order 1 is given by a
function of their
number of co-occurrence and the value/s respective of one or more of the
"value significance
measure/s" (e.g independent probability of occurrence). Several exemplified
such association
strength measure were given by Eq. 16-24. The FIG 2 was also illustrated in
some details in
the section II-III of this disclosure.
Referring to FIG 3 now, it is to show that any composition of ontological
subjects can in
principal be represented by a graph which in this preferred embodiment shown
as an asymmetric
graph. The exemplified graph is corresponded to one of the exemplary
"association strength
matrix", i.e. an ASM, as representative of its adjacency matrix. The nodes
represent the desired
group of OSs and the edge or arrows show the link between the associated nodes
and the values
on the edges are representative of the association strength from one node to
the connected one.
This figure is to graphically exemplify and depicts that compositions of
ontological subjects
and a network of ontological subjects can basically be investigated and dealt
with in the same
manner according to the teachings of the present invention.
In FIG 4, there is shown again another embodiment for the process of
calculating various
value significance measures in more details. As seen the data of the input
composition is
transformed to calculable quantities and data from which, employing the above
methods and
formulations, the desired value significance measures are calculated and/or
are stored in the
storage areas for further use or being used by other processes or programs or
clients.
In reference to FIG 5, it became evident that at this stage, and in accordance
with the
method, and using one or more of the participation matrix and/or the
consequent matrices one
can also evaluate the significance of the OSs by building a graph and
calculating the centrality
power of each node in the graph by solving the resultant eigen-value equation
of adjacency
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
matrix of the graph as explained in patent application 12/547,879 and the
patent application
12/755,415.
FIG 5 therefore shows the block diagram of one basic exemplary embodiment in
which it
demonstrates a method of using the association strengths matrix (ASM)to build
an "Ontological
Subject Map (OSM)" or a graph. The map is not only useful for graphical
representation and
navigation of an input body of knowledge but also can be used to evaluate the
value
significances of the OSs in the graph as explained in the patent application
12/547,879 entitled
"System and Method of Ontological Subject Mapping for knowledge Processing
Applications"
filed on AUG-26-2009 by the same applicant. Utilization of the ASM introduced
in this
application can result in better justified Ontological Subject Map (OSM) and
the resultant
calculated significance value of the OSs.
The association strength matrix could be regarded as the adjacency matrix of
any graphs
such as social graphs or any network of any thing. For instance the graphs can
be built
representing the relations between the concepts and entities or any other
desired set of OSs in a
special area of science, market, industry or any "body of knowledge". Thereby
the method
becomes instrumental at identifying the value significance of any entity or
concept in that body
of knowledge and consequently be employed for building an automatic ontology.
The
VSM 1 2 xk 11 and other mathematical objects can be very instrumental in
knowledge
discovery and research trajectories prioritizations and ontology building by
indicating not only
the important concepts, entities, parts, or partitions of the body of
knowledge but also by
showing their most important associations.
Referring to FIG 6a, 6b, 6c now, they show one graphical representation of the
concept of
the different values of different "value significance measures". As seen
values of different types
of value significance measures (labeled as XY VSM wherein XY is used to show
the different
types of VSM/s) can be shown as a vector in a multidimensional space. Though
XY VSM/s in
general are matrices that might also carry the relational value significances
but still any row or
column (as shown in FIG 6 a) of them can be shown as discrete vectors in a
multidimensional
space. These discreet vectors can also be treated as discrete signals in which
they can be further
be used for investigation of the compositions. Some types of XY VSM, that are
intrinsic, are
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
vectors (e.g. FIG 6b) for which they can readily be used to weigh other OSs or
the partitions of
the composition. Also shown in FIG 6c are some of the vectors that might be
"special conveyer
vectors" labeled with "significance conveyer vectors" in the FIG 6c and are
usually predefined
or predetermined that can be used for filtering out and/or dampening or
amplifying and/or
shaping/synthesizing the VSMs of one or more of the predetermined OSs of the
composition.
FIG 6c demonstrate that special conveyer vectors or VSM have basically the
same
characteristics as other XY-VSM except the values might have been set in
advance.
FIG 7 shows one way of demonstrating (e.g. schematically) how two exemplary
value
significance vectors can be extracted from an exemplary "association strength
matrix" (asm)
which in this instance are also shown to be used to evaluate the associations
of OSs of order 1
(e.g. sentences) to particular OS of order k (e.g. a word or keyword or
phrase). Generally FIG 7
is for further clarification and instantiation of the actual meaning and their
use and the way to
manipulate and use, deal, and calculate the variables and data or mathematical
objects that were
introduced in the previous sections. However, the disclosed processes and
methods with the
given formulations should be enough for those of ordinary skilled in the art
to enable them to
implement, execute, and apply the teachings of the present invention.
An application of the instance demonstration of FIG 7 is that an OS of order
1, can be
selected by the investigator based on its strength of association to one or
more OSs of the order
k. The calculation and the selection method of OSs of order 1 can find an
important application
in document retrieval, question answering, computer conversation, in which a
suitable answer
or output is being south from a knowledge repository (e.g. a given
composition) in response to
the input query or composition. As an example, for showing how to utilize the
disclosed
method/s, an input statement or a query is parsed to its constituent OSs of
order k and from the
association strength matrix (which might be constructed from and for said
knowledge
repository) then the mostly related partitions of the stored composition (i.e.
the knowledge
repository) is retrieved in response of an input query which is a
conversational statement or a
question. For instance, the mostly related partition of the knowledge
repository can be the
partition (OS of order 1) that has scored the highest average or cumulative
association to the
constituent OSs of the input query. The mostly related partition of the
knowledge repository
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
might have scored the highest, for example, after multiplication of the
association strength
vectors of the OSs of the input query in the association strength matrix that
have been built from
the knowledge repository.
Referring to FIG 8 now, it shows, in schematic, a block diagram of an
exemplary system
as well as the process of further clarification as how to use the "value
significances" data of one
or more OSs of particular order to evaluate and calculate the one or more
"value significances"
of OSs of another order using the one or more XY VSM and one or more
participations matrix.
The XY in the FIG 8 is the indication, and can be replaced with the desired
type and number
combination, of the desired "value significance measure". Therefore XY_VSM in
FIG 8 can be
replaced with any of the different types of the "value significance measures"
(such as RVSM,
NVSM, ARASM, RSVM, etc.). The data objects can be stored, if desired, for
later use so that the
pre-calculated data and objects are pre-made and can easily be retrieved for
the corresponding
compositions and the desired application. The pre-made stored data can be used
to accelerate
and speeding up the process of composition investigation in a system that
provide such a
service/s to one or more clients.
Referring to FIG 9 now it shows an exemplary system, process and application
of the
present invention. FIG 9 shows an instance of clustering and ranking, and
sorting of a number
of webpages fetched from the intemet for example, by crawling the internet.
This is to
demonstrate the process of indexing and consequently easily and efficiently
finding the relevant
information related to a keyword or a subject matter. This is the familiar but
very important
application and example of the present invention to be used in search engines.
As seen after
crawling a number of webpage or documents from the interne (or from any other
repository in
fact) the pages/documents/compositions are investigated so that the
associations of the desired
part or partitions of such collections are calculated to other desired OSs of
the collection of the
compositions. Now, in such a exemplary search engine, once a client enter a
query or a keyword,
it would be straightforward to find the most relevant document, page, or
composition to the
input query, i.e. or a target OS.
Accordingly, as discussed in the previous sections, having one or more of the
"association
strength matrix/es" (indicated by XASM) or RVSMs etc., using the disclosed
algorithms make it
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
possible to retrieve the documents with the highest degrees of relevancy to
the input query or
the target OS. This is one of the very important applications and implication
of the disclosed
teachings and materials, since, as is experienced by many users of the
commercial search
engines; the relevancy of retrieved documents to the input query has been and
is a major
challenge in improvement of the search engine performance. However, employing
the
investigation methods of present invention, through its various measures, make
it possible to
quickly and reliably retrieve the most semantically related document/page to
the input query.
Furthermore, some special OSs can be selected for which the association
strength of pages
are to be calculated. For instance, special OSs can be the content words such
as nouns or named
entities. Nevertheless there would be no limitation on the selection or choice
of the target OS
and they can basically be all possible types of words, or even sentences and
higher orders
partitions.
Moreover, through the investigation of crawled pages, either in one step or in
several steps,
OSs of high value significance can be identified so that the whole composition
(i.e. the whole
collection of the documents or pages) can be clustered or categorized into
bodies of knowledge
under one or more target subject matter or head categories (e.g. the high
value OSs of lower
order, such as words or phrases).
The target OSs could usually be the keywords or phrases, or the words or any
combinations
of the characters, such as dates, special names, etc. However in extreme but
useful case the
target OSs of such composition could be the extracted sentences, phrases,
paragraphs, or even
a whole document and the like.
As seen from the teachings of the present invention then it becomes readily
straightforward
to calculate the association and relevancy of each part of such a composition
(such as the
webpages or documents or their parts thereof) to each possible target OSs.
These data are stored
and therefore upon receiving a query (such as a keyword or a question in a
natural language
form, or in the form of a part of text etc.) the system will be able to
retrieve the most relevant
partitions (e.g. a sentence, and/or paragraph, and/or the webpage) and present
it to the user in a
predetermined format and order.
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
Let's exemplify and explain this even in more detail here, when a service
provider system
such as a search engine, question answering or computer conversing, which
comprises or
having access to the system of FIG 9, receives a query from a user, the system
can simply parse
the input query and extract all or some of the words of the input query ( i.e.
the OSs of order
one) then by having calculated the associations strength of rasm_x1'51 one can
easily calculate
the association strength of each of the documents (e.g. wep-pages) to the
words of the input
query, and eventually the documents which have the overall acceptable
association strength
with the selected words of the input query will be presented to the queries as
the most relevant
document or content.
In another exemplary method of retrieval using this embodiment the most
related document
or partition to the input query are identified and retrieved or fetched as
follow:
- extract the OSs (e.g. words) of the input query,
- obtain the rasm_xl'll vector (e.g. the association strength of a words to
each other
obtained from the investigation of the crawled repository of webpages
consisting one
or more webpages/documents) for the input words of the query,
- make a common association strength spectrum or vector for the input words
of the
query by, for example, averaging the rasm_xl-'11 vectors or multiplying them
to each
other,
- use the common association vector to identify the most related or
associated
documents, or sentences to the input query by multiplying the common
association
spectrum with the respective participation matrix (e.g. PM15 for document
retrieval
and PM12 for question answering or conversation as an example).
Moreover most of calculation can be done in advance and even for each target
OSs (though
not as a condition but usually the intrinsically significant OSs can be used
as possible target)
and therefore there could be assembled for each possible target OS a body of
knowledge pre-
made and pre categorized and ready for retrieval upon receiving a query by a
system which has
access to these data and materials. The degree of relevancy of such retrieved
pages to the target
OSs (e.g. the user's Queries) is semantically insured and the relevancy of
such retrieved
materials far exceeds the quality of the currently available search engines.
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
More importantly in a similar manner the engine can return for instance the
document or
the web-page that composed of the partitions of high novelty values, either
intrinsic or relative,
to the target OS/s. Therefore the engine can also filters out and present the
documents or
webpages that have most relevancy to the desired "significance aspect" based
on the user
preferences. So if novelty or credibility or information density of a
document, in the context of
a BOK, is important for the user then these services can readily be
implemented in light of the
teachings of the present invention.
Referring to FIG 10 now, it shows schematically a system of composition
investigations
that can provide numerous useful data and information to a client or user as a
service. Such
output or services in principal can be endless once combined in various modes
for different
application. However in the FIG 10 a few of the exemplary and important and
desirable outputs
are illustrated. The FIG 10 illustrates a block diagram system composed of an
investigator
and/or analyzer and/or a transformer and/or a service provider that can
receive or access a
composition and provide a plurality of data or content as output. The
investigator in fact
implement at lease one of the algorithms of calculating one of the measures in
order to assign a
value on the part or partitions of the compositions and based on the assigned
value process one
or more of the partitions or OSs of the particular order as an output in the
form of a service or
data. The output could be simply one or more tags or OS/s that the input
composition can be
characterized with, i.e. significant keywords of the composition. In this
instance, the significant
keywords or labels are selected based on their values corresponding to at
least one of the
aspectual XY VSM, i.e. one of the value significance measures.
As another example, the output or outcome of the investigator of FIG 10, could
be to
provide the partitions of the input composition which have exhibited intrinsic
value
significances of above a predetermined threshold. Another output could be the
novel parts or
the OSs of the compositions that scored a predetermined level of a particular
type of novelty
value significance. Or the output could be the noisy part of a composition or
a detected spam in
a collection of compositions etc.
Several other output or services of the system of FIG 10 are depicted in the
FIG 10 itself
which are, in light of the foregoing, self explanatory.
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Patent Application of Hannid Hatanni-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
Referring to FIG 11 now, it shows another instance and application of the
present invention
in which the process, methods, algorithms and formulations used to investigate
a number of
news feeds and/or news contents automatically and present the result to a
client. In this
exemplary but important application system, the news are being first
categorized automatically
through finding the significant head-categories and consequently clustering
and bunching the
news into or under such significant head-categories and then select one or
more partitions of
such cluster to represent the content of that clustered news to a reader. Head-
categories can
simply being identified, by evaluating at least one of the significance
measures introduced in
the present invention, from those OSs that have exhibited a predetermined
level of significance.
The predetermined level of significance can be set dynamically depends on the
compositions of
the input news.
It is important to notice that some of data in respect to any of these
features (e.g. association
of OSs) can be obtain from one composition (e.g. a good size of body
knowledge) in order to
be used in investigation of other compositions. For instance it is possible to
calculate the
universal association of the concepts by investigation the whole contents of
Wikipedia (using,
for instance, exemplary teachings of present invention) and use these
data/knowledge about the
association of concept in calculating a relatedness of OSs of another
composition (e.g. a single
or multiple documents, or a piece or a bunch of news etc.) to each other or to
a query.
Moreover other complimentary representations, such as a navigable ontological
subject
map/s, can accurately being built and accompany the represented news. Various
display method
can be used to show the head-categories and their selected representative
piece of news or part
of the piece of the news so that make it easy to navigate and get the most
important and valuable
news content for the desired category. Moreover the categorization can be done
in more than
one steps wherein there could be a predetermined or automatic selection of
major categories
and then under each major category there could be one or more subcategories so
that the news
are highly relevant to the head category or the sub-categories or topics.
Furthermore many more forms of services can be performed automatically for
this
exemplary, but important, application such as identifying the most novel piece
of the news or
the most novel part of the news related to a head category or, as we labeled
in this disclosure,
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
to a target OS. Such services can periodically being updated to show the most
updated
significant and/or novel news content along with their automatic
categorization label and/or
navigation tools etc.
Referring to FIG 12 now, it shows one general embodiment of a system
implementing the
process, methods and algorithms of the present invention to provide one or
more services or
output to the clients. This figure further illustrates the method that a
particular output or service
can in practice being implemented. The provider of the service or the outputs
can basically
utilizes various measures to select from or use the various measures to
synthesize the desired
sought after part's of an input compositions. A feature to be noticed in this
embodiment is that
the system not only might accept an input composition for investigation but
also have access to
banks of BOKs if the service calls for additional resources related to the
input composition or
as result of input composition investigation and the mode of the service.
Moreover as shown
the exemplary embodiment of system of FIG 12 has a BOK assembler that is able
to assemble
a BOK from various sources, such as interne or other repositories, in response
to an input
request and performs the methods of the present invention to provide an
appropriate service or
output data or content to one or more client. The filtration can be done is
several parallel or
tandem stages and the output could be provided after any number the step/s of
filtrations. The
filters Fi, F2 ,...Fn can be one of the significance measures or any
combinations of them so as
to capture the sought after knowledge, information, data, partitions from the
compositions. The
output and the choice of the filter can be identified by the client or user as
an option beside
several defaults modes of the services of the system.
Another block in the FIG 12 to mention is the post-processing block that in
fact has the
responsibility to transform the output of the filter/s into a predetermined
format, or transform
the output semantically, or basically composing a new composition as a
presentable response
to a client from the output's of the filters of the FIG 12. Also shown in this
exemplary
embodiment there is a representation mode selection that based on the selected
service the
output is tailored for that service and the client in terms of, for instance,
transmission mode,
web-interfacing style, frontend engineering and designs, etc.
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
Furthermore the exemplary system embodiment of FIG 12 shows a network bus that
facilitate the data exchange between the various parts of the system such as
the BOK bank (e.g.
containing file servers) and/or other storages (e.g. storages of Losi Los2,
Los3õ etc. and/or list
storage/data wherein Los stands for List of the Ontological Subjects and, for
instance, Losi
refers to the list of the OSs of order 1) and/or the processing engine/s
and/or application servers
and/or the connection to internet and/or connection to other networks.
FIG 13 shows another general embodiment block diagram of a system providing at
least
one service to a client. In this figure there is a composition investigator
wherein the investigator
has access to a bank of bodies of knowledge or has access to one or modulus
that can assemble
a body of knowledge for client. Such said module can for example use search
engines to
assemble their BOK or from another repository or database. The system can also
provide one
or more of the services of the FIG 10 to a client. For instance the system is
connected to the
client through communication means such as private or public data networks,
wireless
connection, internet and the like and either can receive a composition from
the client or the
system can assemble a composition or a body of knowledge for the client and/or
the system can
enrich or add materials to the client's input composition and perform the
investigation and
provide the result to the client. For example, by investigating the input
composition from the
client or user, the system can automatically identifies the related subject
matters to the input
composition and go on to assemble one or more BOK related to at least one of
the dominant
OSs of the input composition and offer further services or output such as the
information
regarding the degree of novelty of the input composition in comparison to one
or more of said
BOKIs and/or score the input composition in terms of credibility or overall
score of the merits
of the input compositions in comparison to the said BOKIs and/or identify the
substantially
valuable and/or novelty valuable part or partitions of the input composition
back to the user or
other clients or agents. In light of the disclosed algorithms and method/s of
the composition
investigation there could be provided a software/hardware module for
composition comparisons
that provide one or more of the services or the output data of the just
exemplified application.
The mentioned exemplary application and service can, for instance, be of
immense value
to the content creators, genetic scientists, or editors and referees of
scientific journals or in
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of ontological subjects and intelligent systems therefrom".
principal to any publishing/broadcasting shops such as printed or online
publishing websites,
online journals, online content sharing and the like.
Such a system can further provide, for instance, a web interface with required
facilities for
client's interaction/s with the system so as to send and receive the desired
data and to select one
or more desired services from the system.
For instance it can be used as a system of interactive and social knowledge
discovery as
introduced in the US patent Ser. No. 12/955,496 now the Us Patent No.
8,775,365 entitled
"Interactive and Social Knowledge discovery Sessions" which was incorporated
entirely as a
reference in this application.
Also as shown in the FIG 13, other optional modulus can be made available to
the client
that uses the main composition investigator and or the BOK assembler or BOK
banks. A client
can, for examples, be a machine, human, another software agent, an intelligent
being, a remote
server, or the like. One of such optional modulus can be a module for client
and computer or
the client and system converse or conversation. The conversations is done in
such a way that
the system of this exemplary embodiment with the "converse module" receives an
input from a
client and identifies the main subject's of the input and provide a related
answer with the highest
merit selected from its own bank of BOK1s or a particular BOK or an available
composition.
The response from the system to the client can be tuned in such a way to
always provide a
related content according to a predetermined particular aspect of the
conversation. For example,
the client might choose to receive only the content with highest novelty yet
credibility value
from the system. In this case the "converse module" and/or the investigator
module will find
the corresponding piece of content (employing one or more of the "XY value
significant
measure") from their repositories and provided to the user. Alternatively, for
instance, the user
can demand to receive the most significant yet credible piece of knowledge or
content related
to her/his/it's input. The client/system conversation, hence, can be
continued. Such conversation
method can be useful and instrumental for variety of reasons/applications such
as entertainment,
amusement, educational purpose, questions and answering, knowledge seeking,
customer
relationship management and help desk, automatic examination, artificial
intelligence, and very
many other purposes.
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
The system, for instance can be used as a system of providing or generating
visual and/or
multimedia content as introduced the US patent application Ser. No. 12/908,856
entitled
"System And Method Of Content Generation", filed on Oct. 20, 2010, and or
using the value
significance measures and the maps and indexes to automatically generate
content
compositions as introduced in the U.S. patent application Ser. No. 12/946,838,
filed on Nov 15,
2010, now US Patent No: 8,560,599 B2 entitled: "Automatic Content Composition
Generation",
which were incorporated entirely as references in this application
In light of the teaching of this disclosure, such exemplified modules and
services can
readily be implemented by those skilled in the art by, for instance, employing
or synthesizing
one or more the value significance measures, and the disclosed methods of
investigation,
filtration, and modification of composition or bodies of knowledge.
FIG 14, further exemplifies and illustrates an embodiment of a system of
composition
investigation that one or more client are connected to the system directly and
one or more clients
can optionally be connected to the system through other means of
communications such as
private or public data network such as wireless networks or internet. In this
instance the whole
system can be a private system providing such services to its user or the
system is composed of
several hardware and necessary software modules over a private network wherein
the users can
use the services of composition investigation by the system directly or over
the network. Such
a system can in one configuration being characterized as a private cloud
computing facilities
capable of interacting with clients and running the one or more of the process
and algorithms
and/or implement and execute one or more of the relational value significance
calculations
processes or implementation of one or more of the formulas or equivalent
process in their
software module/s to provide data/content and/or a desirable service of
composition
investigation to one or more client.
FIG 15, shows another exemplary instance of ubiquitous system and service
provider in
which the system can/might be a distributed system and is using resources from
different
locations in order to perform and provide one or more of the services. One or
more of the
function performs as shown in FIG 15, might be physically located across a
distributed network.
For instance one or more of the calculations, or one or more of the servers,
the front end server,
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of ontological subjects and intelligent systems therefrom".
or the client's computer or device can be located in different places and
still the services is
performed over a distributed network. In this configuration an ISP who is
facilitating the
connection for a client to such a distributed network is regarded as the
service provider of such
service. Therefore a facilitator that facilitated (e.g. through a switch,
router or a gateway etc.)
at least some of the request or response data either from the client or from
any part of such a
distributed service is regarded as instance of such a service provider system.
The data/information processing or the computing system that is used to
implement the
method/s, system/s, and teachings of the present invention comprises storage
devices with
more than 1 (one) Giga Byte of RAM capacity and one or more processing device
or units (
i.e. data processing or computing devices, e.g. the silicon based
microprocessor, quantum
computers etc.) that can operate with clock speeds of higher than 1 (one) Giga
Hertz or with
compound processing speeds of equivalent of one thousand million or larger
than one
thousand million instructions per second (e.g. an Intel Pentium 3, Dual core,
i3, i7 series, and
Xeon series processors or equivalents or similar from other vendors, or
equivalent processing
power from other processing devices such as quantum computers utilizing
quantum computing
devices and units) are used to perform and execute the method once they have
been
programmed by computer readable instruction/codes/languages or signals and
instructed by
the executable instructions. Additionally, for instance according to another
embodiment of the
invention, the computing or executing system includes or has processing
device/s such as
graphical processing units for visual computations that are for instance,
capable of rendering
and demonstrating the graphs/maps of the present invention on a display (e.g.
LED displays
and TV, projectors, LCD, touch screen mobile and tablets displays, laser
projectors, gesture
detecting monitors/displays,3D hologram, and the like from various vendors,
such as Apple,
Samsung, Sony, or the like etc.) with good quality (e.g. using a NVidia
graphical processing
units).
Also the methods, teachings and the application programs of the presents
invention can be
implement by shared resources such as virtualized machines and servers (e.g.
VMware virtual
machines, Amazon Elastic Beanstalk, e.g. Amazon EC2 and storages, e.g. Amazon
S3, and
the like etc. Alternatively specialized processing and storage units (e.g.
Application Specific
Integrated Circuits ASICs, system/s on a chip, field programmable gate arrays
(FPGAs) and
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of ontological subjects and intelligent systems therefrom".
the like) can be made and used in the computing system to enhance the
performance and the
speed and security of the computing system of performing the methods and
application of the
present invention.
Moreover several of such computing systems can be run under a cluster,
network, cloud, mesh
or grid configuration connected to each other by, data bus/es, communication
ports and data
transfers apparatuses such as switches, data servers, load balancers,
gateways, modems,
internet ports, databases servers, graphical processing units, storage area
networks (SANs
and the like etc. The data communication network to implement the system and
method of the
present invention carries, transmit, receive, or transport data at the rate of
10 million bits or
larger than 10 million bits per second;"
"Furthermore the terms "storage device, "storage", "memory", and "computer-
readable
storage medium/media" refers to all types of no-transitory computer readable
media such as
magnetic cassettes, flash memories cards, digital video discs, random access
memories
(RAMSs), Bernoulli cartridges, optical memories, read only memories (ROMs),
Solid state
discs, and the like, with the sole exception being a transitory propagating
signal.
These applications and systems are presented to exemplify the way that the
present invention
method of investigation might be employed to perform one or more of the
desired processes to get
the respective output or the content, answer, data, graphs, analysis, and
service/s etc. Several modes
of services and further applications are exemplified herebelow.
= The processes and systems of FIGs. 8-15 can be an on premises system, an
intelligent being, or a network system of computation and processing, storage
medium, displays and interfaces, and the associated software.
= In another instance the systems and processes of the FIGs. 8-15 can be a
remote
system providing the service in the form of cloud environment for one or more
clients providing one or more the services mentioned above.
= Yet in another instance the system can be a combination of an on premises
private
cloud/machine computation facilities connected to a public cloud service
provider.
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
These familiar mode of operation characterized as public and/or private and/or
hybrid cloud computing environment (either distributed or central, on premises
or
remote, private or public or hybrid) is known to the skilled to art and the
disclosed
methods of investigations of compositions of ontological subjects can be
performed in variety of topologies which is regarded as service provider
system
employing one or more of the generating methods/s of output data respective of
one
or more of the disclosed methods of the investigation of a composition of
ontological subjects.
= An interesting mode of service is when for an input composition and after
investigation the system yet provides further related compositions or bodies
of
knowledge to be looked at or being investigated further in relation to the one
or
more aspect of the input composition investigation. Another service mode is
that
the system provides various investigation diagnostic services for the input
composition from user.
= Another mode of use is when an intelligent being make connection or
communicate
with the system of composition investigation (i.e. the brain) by way of
communication networks to provide desired services (e.g. conversing, telling
stories, talking, instructing, providing consultancy, generating various
content,
manufacturing, etc.). In another instance the currently disclosed method/s and
system/s is implemented within the intelligent being or used to realize new
intelligent beings.
= Furthermore the method and the associated system can be used as a
platform so that
the user can use the core algorithms of the composition investigation to build
other
applications that need or use the service of such investigation. For instance
a client
might want to have her/her website being investigated to find out the
important
aspects of the feedback given by their own users, visitors or clients.
= In another application one can use the service to improve or create
content after a
through investigation of literature.
= In another instance the methods and systems of the present invention can
be
employed to provide a human computer conversation and/or computer/computer
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of ontological subjects and intelligent systems therefrom".
conversation such as chat-bots, automatic customer care, question answering,
fortunetelling, consulting or any general any type of kind of conversation.
= In another mode a user might want to use the service of the such system
and
platform to compare and investigate her/his created content to find out the
most
closely related content available in one or more of such content repositories
(e.g. a
private or public, or subscribed library or knowledge database etc.) or to
find out
the score of her/his creation in comparison to the other similar or related
content.
Or to find out the valuable parts of her/his creation, or find a novel part
etc.
As seen there could be envisioned numerous instance of use, products, beings,
and applications
of such process and methods of investigating that can be implemented and
utilized by those of
skilled in the art without departing from the scope and sprit of the present
invention.
II-VIII- ARTIFICIAL INTELLIGENT SYSTEMS USING NEURAL NETWORKS
As disclosed in the U.S. patent application Ser. No. 14/607,588, filed on Jan
28,215, entitled "Association
strengths and value significances of ontological subjects of networks and
compositions" a network of
objects is considered a composition and vice versa. Accordingly the methods of
investigation disclosed
here are applied to build new applications, services and products. Accordingly
a network of ontological
subjects can be a representative for a composition and vice versa. In
particular artificial neural networks
are therefore a form or a representative of a composition of ontological
subjects itself whose associations
of its ontological subjects ( e.g. connections between nodes of the network)
are to be known.
The popularity of the neural networks and the so-called deep learning is due
to its potential ability
to train a network of connecting nodes to become able to map a certain set of
data (e.g an input dada) to
a desired set of data (e.g. the output data).
Currently the connection weight between nodes of a neural network is obtained
by various training
algorithms and processing which are generally rooted in stochastic gradient
decent type of algorithms.
These methods are prone and notorious for non-converging (..which result in
relinquishing many
of the useful parts of the neural net concept in general) or being non
reliable for critical tasks (they can
be fooled by slight noise introduction in the input data of such).
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of ontological subjects and intelligent systems therefrom".
In training of such system having a good initializing of the sate/s of such
network (e.g the initial
weight or weight function between connecting nodes) is of vital importance for
the success of the
training, ability and overall performance of the trained neural network
system.
Referring to Fig 11-A now, here we like to use the disclosed methods for
building and
training an artificial neural network for various e applications such as
categorizations,
recognition, content generation or utterance generation . In here we show an
exemplary
multilayer neural network comprises of a number of neurons in each layer. The
whole network
can have very many layers (e.g. hidden layers to provide extra degrees of
freedom for
optimization) each node can be considered or assigned with an ontological
subjects of
predefined order (e.g. such as each node in the first layer can be represented
of a textual word)
the second layer.
Each node (e.g. a neuron or perceptron) in each layer is connected to a number
of other
nodes in its preceding layer and to a number of nodes on its consequent layer.
The role of neural
network is to learn the impact of each input/neuron to other neuron in other
layers either directly
or indirectly (through hidden layers).
The fundamentals of neural networks and more recently deep learning neural
networks are
straightforward and is known in the literature. Basically the aim of learning/
or training of a
neural network is to find or adjust the weight/impact of each node to/from its
connecting nodes.
The training of any reasonably useful neural network however is not a trivial
undertaking
needing a large number of highly specialized processing devices (e.g expensive
Graphical
Processing Units) and a long training time.
In Fig 11-A, it can be shown that a matrix of N x M will map the N inputs of
the network
in Fig 11-A to the desired number of outputs (e.g M).
Lets call such a matrix A which would be aN x M matrix and itself can be
decomposed to
number of (..in fact it can be decomposed to infinite number of other
matrices) like the
followings:
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of ontological subjects and intelligent systems therefrom".
Matrix A with dimetion of N x M
= Al ( dimention: N x M1) x A2( dimentions: M2 x M3)
x .......An( dimentions: Mn x M)
wherein Al, A2, ...An are matrices with dimensions specified in the above
equations. Each
intermediate matrix can be corresponded to the connections of nodes of
adjacent layers. These
intermediate matrices show the connection and the weight of the connections
between nodes of
adjacent layer or back propagating connections from other layers.
Computationally and in
practice training of a neural network starts/initialized with a randomly
populated matrices and
the values are changes and varied through various computational algorithms
until the desired
results are achieved satisfactorily. Such desired results from the network
could be that the
network become able to classify an image correctly with high degree of
probability, or
distinguishes an audio signal and extract or convert the audio signal to its
corresponded or
equivalent text, and/or translating text/voice between languages etc.
Regardless of the application of a neural network, however, each of these
intermediate,
matrices that will collectively make the whole neural network to perform a
task, are to be fund
which is the goal of neural networks learning algorithms. It is conceptually
easy to see that if a
node (i.e. a neuron) is connected to/from another node so they would have some
sort of
relationships and or, using the terms of this disclosure, some types of
associations and
relationship with each other.
Accordingly it is easy to see that the goal of neural of network training
algorithms is in fact
trying to find a degree or a force intensity or influence or in other word the
strength of the
associations between the nodes that make up the neural network.
Now considers that nodes of the first layer are corresponded to Ontological
subjects of
order i and the nodes of a second layer are corresponded or representatives of
Ontological
subjects of order] (i and] can be the same or equal) and...
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of ontological subjects and intelligent systems therefrom".
For instance, in an exemplary embodiment shown in FIG 11-A, nodes of the first
layer can
be regarded or been representative of textual words of a natural language such
as words of
English languages as input to a system of networks of nodes (e.g. Neural
Networks, the so
called deep learning neural nets, or any other network of objects with some
data processing
function).. The nodes in the second or third layer can be representatives of
sentences or English
words again (i=j) whereas the nodes of third layer can be representative of
word phrases,
sentences, paragraphs, textual templates (sentence template, paragraph
templates containing
one or more words), and so on. Same can be said for other layers between the
input and output
layer. (Same can be done for various sets of partitions of images and pictures
as will be
discussed ore specifically in the next section).
Currently to find such relationship between theses nodes the neural net needs
to be trained
with huge number of data sets and corpuses in order to have relatively a
meaningful working
neural network and sensible output.
Without going into the details of shortcoming of such training and drawbacks
of neural
network to perform intelligent tasks, here it is aimed to use the data objects
(e.g. various
association strength matrices, various significance values etc.) of this
disclosure which are
obtained or built by exercising the teachings of this disclosure to build a
neural networks both
in hardware or software shape with the initial connections and weights are
obtained by
calculating for example ASM of different types and order and if it is needed
further train the
neural network to even function better. Said neural network further can be
implemented as
various classes/types of recurrent neural networks, convolutional neural
networks, recursive
neural networks, neural history compressor, feed forward neural networks and
the like.
The advantage of using ASM/s to build a neural network is threefold as
outlined next,
1. First: using the data of ASM/s we would know which nodes has to be
connected
to each other rather than blindly connecting every node to every other node.
Currently to get a satisfactory result one have to have very large number of
neurons
at each layer ( in order of millions to billions) and connecting the nodes to
each
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of ontological subjects and intelligent systems therefrom".
other as much as possible in order to have enough parameters to play with to
eventually synthesize an unknown function (e.g. the artificial intelligent
brain).
Using the data of associations from this disclosure therefore can reduce the
size of
the neural network significantly.
2. Secondly, since the data (e.g the entries of ASM matrix or connection
weight
between the nodes) are close to their actual values in really world, further
adjustments to improve the performance of the artificial neural network would
converge much quicker while the performance of the whole network (as an
artificial
brain) would be significantly enhances.
3. Thirdly, Since we have introduced various data objects and various types of
associations and relationships between the ontological subjects of a
composition or
very large set of compositions the neural network become programmable and
therefore the designer of such systems has control and insight into to working
mechanics of the artificial intelligent system (e.g. a robot or self-driving
car/robot
etc) which employs an artificial network of ontological subjects (e.g neural
network). In this way the designer of such system have advance knowledge and
expectation form the system whereas currently the neural networks are trained
by
brute forces and sheer processing power of processing devices such as NVidia
graphical processing accelerators.
To summarize this section the disclosure introduces an artificial intelligent
system which uses the
various data objects of from the investigator of Fig 10 to build and train
further a network of
ontological subjects ( a neural net is an instance of network of ontological
subjects) to perform
intelligent tasks and to implement machine learning by investigating one or
more bodies of
knowledge to learn about the world.
There could be two different systems to build the Al system here. Onne is that
the investigator is
part of the system and second is that the Al system ( e.g. The hardware or
software system) uses
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of ontological subjects and intelligent systems therefrom".
the data objects of the investigator in order to learn and train itself much
faster, using minimal
number nodes as necessary and much efficient while become much more
affordable.
Such a system then is incorporated into mechanical systems such as special
purpose or general
purpose robots and intelligent systems and machines.
II-IX- INVESTIGATION VISUAL COMPOSITIONS AND IMAGE PROCESSING
In this section another instantiation, application and system of image
processing is
presented. The system of image processing is basically the system of FIG 10,
wherein, as shown
in Fig 11-B, exemplary illustration is given as how to apply the methods of
this disclosure to
process image data and gain them.
After the processing of the image/s, the system of image processing can
classifies related
or similar images, through calculating various Association and Significance
values of
Ontological Subjects of visual nature and order.
As seen in FIG 11-B, one can initially partitions an image or a movie frame
down to its
individual RGB components of its pixels as Ontological subjects of order zero,
then regards a
pixel as composition of RGBs or more conveniently as OSs of order 1, then, for
example, every
two adjacent pixels (horizontally and/or vertically as desired) as OSs of 2,
and every 2 of OSs
of order 2 as OSs of order 3 and so on. In this particular illustrations, for
example, an OSs of
order k is in fact composed of 24'2) (for k >2) pixels. Obviously one may
elect to partition the
image in another fashion and user different order for any certain number of
pixels.
In this way we become able to transform the information of a picture into
existence of such
ordered ontological subjects into each other through constructing data objects
or one or more
data structures corresponding to the participation matrix/matrices of various
order as described
and defined several times along this disclosure and/or the incorporated
references herein.
Further the lists of OSs of particular order defined for visual objects can be
a set (all
identical OSs represented with one of such) or be listed as they appear in the
picture.
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of ontological subjects and intelligent systems therefrom".
Setting the ordered ontological subjects of the picture will make the PMs less
data intensive
resulting faster processing and shortening the image processing task thereof.
Furthermore
sometimes said setting can also enhance the functionality of the process and
lessen the clutters.
For instance, if the desired function of the process is to categorize the
visual objects, setting the
OSs may help to reduce unnecessary noise beside the data processing effect.
For some other applications however, it might be desirable to keep all the OS
s of any order
as they appeared in the picture. In this case index of that OS s in a PM also
bears the geometrical
information of that OS s (partitions of the picture) in the picture.
For instance the index of the ontological subjects (the index of the column or
the rows that
each OS will be represented in the participation matrix) bears a very
important information
about a picture and can be used geometrically to characterize a picture. For
instance the ratio of
the j index of significant OS s of order 3 of the picture can be used as
further information to
characterize the picture. New data objects and Matrix/es can be constructed to
convey the
information of some of the selected OS s of certain order of the image
frame/picture respect to
each other. Furthermore gain, such geometrical information and/or their ratio
can be normalized
so that they can be used for comparing to other processing needs (correlating
a picture in a
standard way to a group of other pictures).
Again, the data objects of the present invention (e.g. varicose PMs, ASMs,
VSMs, vectors
or matrices) can be adequately described as being a representation of points
in a Hilbert space
and linear transformations of the data objects does not have drastic effect on
the quality and
continuity of the investigation results. Most other transformation (such as
rotating an image, i.e.
rotating the data of its corresponding participation matrix, or other
mathematical operations on
the data objects) also would not cause a discontinuity type of effect on the
behaviour of the
result of desired data, e.g the result of a novelty detection or finding
significant
partitions/segments or edge detection etc, of an image. In other words the
disclosed image
processing method is much more robust and process efficient than the image
processing with
neural networks, or deep learning, convolutions neural nets, and classical
image processing
methods.
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Nevertheless as is the case with the textual compositions, the result of
investigation of
visual compositions, e.g. the presented image processing, can be used to build
a more efficient
and compact neural networks than building a heuristically large neural
network. Moreover the
data objects that are generated after investigations of a body knowledge,
composed of a number
of images, can be used to initialize the neural networks for further training.
Since the data of
the investigation results (e.g. ASMs, VSMs, RASMs and other data objects of
this disclosure)
like) are obtained from existing and real images (or in general exhibiting
ontological subjects
rather than randomly possibly existing Ontological subjects) a deep learning
network built and
initialized (by using the data of the presented investigation method of
compositions of
ontological subjects) is more likely to converge, and converge faster.
The process is efficient in doing intelligent actions and decision making
based on a
received or input image/picture. Another advantage of using the present
invention as a method
of image processing in application ranging from computer vision, navigation,
categorization,
content generation, gaming and many more, is that the method/s is less
sensitive to the
orientation and angle and almost invariant since many data objects are built
during the
investigation that are assigned to segments of different aasizes of the image.
Accordingly using
or more of these data objects or a combination of different ASM/VSM measures
and the
information that are extracted from the images during the investigation
process, one can assign
a distinguishable signature to an input images.
Once the image is partitioned into segments of predefined sizes or pluralities
of ontological
subjects of different orders calculating then obtaining data objects of
interests become similar
to the described in detailed methods for the textual compositions (see Eq. 1-
64).
Accordingly the system of image processing based on the teaching of this
disclosure
become able to provide all functionalities of FIG. 10. An exemplary
application therefore would
be in computer vision for clustering or classification of images
characterization of images, and
then acting upon such characterization and recognition.
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
In particular, for robot visions, autonomous robots, intelligent expert (e.g.
medical
assistant robots), autonomous or semi-autonomous transportation robots (e.g.
self-driving car,
truck, drone, self-flying objects, etc.).
Once an image is characterized and its relation to a cluster, category or
class become known (e.g.
see the incorporated co-pending US patent application # 15/589,914) a
system/machine,
comprising the images processing/investigation of the present disclosure, can
issue further
instructions or signals to be used by other systems or parts (e.g. another the
machine, software,
robot, intelligent being etc). Such systems/machines can therefore achieve a
cognition and
understanding of their surrounding environment. Further using the present
disclosure method of
investigation of compositions such systems and machines are capable of
conversing and
exchanging data and knowledge not only with other machines but also with human
by
conversing with human clients through human consumable languages or content
such as voice or
machine generated multimedia content.
For instance using the Novel relational associations measure (E.q. 1-38 and 39
onwards)
the investigator system of Fig 10 become able to distinguish movements and
their speed ( as
shown in FIG 11-B visual OSs can be traced by their indices in the partitioned
images and
therefore partitions of the consequent images of live a camera (i.e. movie
frames) can be traced
and their identity and motion can be calculated by using their indices in the
partitioned image.
One particular use of the methods and algorithm of this disclosure in this
would be
ranking of the images based on relational value significances using
association strengths values
of Ontological Subjects of different order.
An interesting system is for image recognition when ranking an input image as
how that
could be related to an ontological subjects ( for example how an image is
close or contain certain
object or living thing etc.) for instance whether there is tree in the image.
In such system for this application the system of FIG 10 comprising data
processing or
graphical processing units have the details of a tree picture along with
partition as number of
sets of ontological subjects of predefined order as been illustrated in FIG 11-
b.
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
Then among a body of compositions of images we can identify whether an input
images
contain certain ontological subject (considering that one can regard a whole
image of tree/tress
as OS of order 4, 5, or higher) then its constituent partitions such single
pixels as OS order 1,
2 pixel partitions as set of OSs of order 2, 4 pixel partitions as set of OSs
of order 3, 16 pixels
partitions as set of OSs of order 4 and so on.
One can find the associations of the partitions of the picture and using some
or all the Eqs.
1-64 to build data structures, programing a GPU, program an FPGA, design a
system on chip,
design and build an application specific computing devices such as ASIC using
silicon or III-V
materials, a data processing apparatus comprising one or more computing or
data processing
devices, and to evaluate or score or rank the relevancy of an input
image/picture to a target or
desired image/picture, category, concept, function, signal, or instructing a
machine or order a
machine to perform a desired task or operations.
For example how closely an input image or picture is related to certain
entity/ies, like a
Cat, a Tree, a House, A car, A passenger, a movable objects (as the target
Ontological subject).
Or when there are very number of images then use the method for classification
and
categorization of images.
Of course the image/pictures can be preprocessed by known digital signal
processing to do
for example, rotate the input picture once or more with certain angle, change
the orientation,
resize the image/picture to a predefined pixel size, or a desired height and
width, or predefined
dimension (e.g every picture transformed re scaled, or resizes to 320 * 320
pixels or to a 1000
by 1000 pixels, or one Mega pixels etc.) Further the range of possible
combinations (R, G, B),
with or without the pixel depth data, can be changed or reduced. For example
the image/picture
can be transformed to gray scale only, or range of pixel color be reduced to a
desired number
of colors, e,g. from 256 x256 x256 number of colors be reduced to 16 x16 x16
number of colors
or the like.
Using the novel type of association or novel relational association, a
computer vision
system is built using the one or more of the investigation methods of this
disclosure or using
the data objects of the investigator to interpret and track the novelty to
their corresponding
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Patent Application of Hamid Hatami-Hanza for "Methods and systems for
investigation of compositions
of ontological subjects and intelligent systems therefrom".
ontological subjects (e.g. a cat is moving near a tree) in order to build a
computer vision system
to be used in systems requiring vision cognitions (e.g. using in humanoid
Robots and/or self-
deriving car/robots or drowns security systems etc.)
In practice, the data volume an image frame or an image file is way more than
the data of
an average text file. Accordingly the processing time of an image frame
especially if it is a high
definition image, is considerably higher. Also consider that usually the image
in some scenarios
or embodiments is processed with a large number of other pictures of the same
category or a
diverse group or number of images.
Therefore, in one exemplary method, application, and system of image
processing with
teachings of this disclosure we use graphic processing units, each having one
or more processing
cores, coupled with enough random access computer readable memories (e.g.
RAMs) to
accelerate the computing speed.
One or more graphic processing units are programed to receive an image frame,
for
instance from a video port, process the image encoded image data to partition
the image and
extract the constituent Ontological subjects of different order, build the
participation matrix/es,
build one or more Association strength measure between ontological subjects of
the said image.
The ASM could be calculated for Ontological subjects of the same order or
different order, each
order corresponds to partition or a segments of various size of the image ( as
described before).
Further building data structures corresponding to value significance of the
portions of at least
one order. Further calculate other data objects of various type such RASM,
RNASM, VSMs,
NVSMs, and any other desired data objects expressed by Eq. 1-65 to investigate
the image or
group of images as outlined in FIG 10 for example. And further execute the
instructions by the
processing units to do at least one of the exemplary applications disclosed in
this disclosure
(such as clustering a large number of images into one or more categories,
novelty detection,
summarization, recognition, tagging, transforming to text, reconstruction of
an image with
certain desired features, construction of other images, new image creation
etc.) or further
process the image to do other desirable functions based on the data of the
investigation results.
The processing units further or coupled with other processing devices can
control other
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
machines, artificial limbs, robots or decide on further actions and/or
executing other functions
and processing.
II-X-SUMMARY
The disclosed frame work along with the algorithms and methods enables the
people in
various disciplines, such as artificial intelligence, robotics, information
retrieval, search engines,
knowledge discovery, genomics and computational genomics, signal and image
processing,
information and data processing, encryption and compression, business
intelligence, decision
support systems, financial analysis, market analysis, public relation
analysis, and generally any
field of science and technology to use the disclosed method/s of the
investigation of the
compositions of ontological subjects and the bodies of knowledge to arrive the
desired form of
information and knowledge desired with ease, efficiency, and accuracy. Since
the disclosed
underlying theory, methods and applications are universal it is worth to
implement in the system
of executing the methods and products directly on processing chips/devices to
further increase
the speed and reduce the cost of such investigations of compositions. In one
instance, for
example, the data processing operations (e.g. vector/matrix manipulations,
manipulating data
structures, association spectrums calculations and manipulation, etc.) and
even storage of the
data structures, is implemented with designs of Application Specific
Integrated Circuits
(ASICS), or Field-Programmable Gate Arrays, (FPGA), or the system-on-chip,
based on any
computing and data processing device manufacturing platforms and technologies,
such as
silicon based, III-IV semiconductors, and quantum computing artifacts to name
a few. Similarly,
if the disclosed methods of the investigation and applications are going to be
used in/with
implementing neural or cognitive based type of computations, still one can
implement the
system on such chips and by said technologies. Accordingly, those competent in
the art can
implement the disclosed methods for various applications/products in/with
various data
processing device manufacturing and designs on the physical material level.
The invention provides a unified and integrated method and systems for
investigation of
compositions of ontological subjects. The method can be implemented language
independent
and grammar free. The method is not based on the semantic and syntactic roles
of symbols,
words, or in general the syntactic role of the ontological subjects of the
composition. This will
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investigation of compositions
of ontological subjects and intelligent systems therefrom".
make the method very process efficient, applicable to all types of
compositions and languages,
and very effective in finding valuable pieces of knowledge embodied in the
compositions.
Several valuable applications and services also were exemplified to
demonstrate the possible
implementation and the possible applications and services. These exemplified
applications and
services were given for illustration and exemplifications only and should not
be construed as
limiting application. The invention has broad implication and application in
many disciplines
that were not mentioned or exemplified herein but in light of the present
invention's concepts,
algorithms, methods and teaching, they becomes apparent applications with
their corresponding
systems to those familiar with the art.
Among the many implications and application, the system and method have
numerous
applications in knowledge discovery, knowledge visualization, content
creation, signal, image,
and video processing, genomics and computational genomics and gene discovery,
finding the
best piece of knowledge, related to a request for knowledge, from one or more
compositions,
artificial intelligence, realization of artificially or new intelligent
begins, computer vision,
computer or man/machine conversation, approximate reasoning, as well as many
other fields
of science and generally ontological subject processing. The invention can
serve knowledge
seekers, knowledge creators, inventors, discoverer, as well as general public
to investigate and
obtain highly valuable knowledge and contents related to their subjects of
interests. The method
and system, thereby, is instrumental in increasing the speed and efficiency of
knowledge
retrieval, discovery, creation, learning, problem solving, and accelerating
the rate of knowledge
discovery to name a few.
It is understood that the preferred or exemplary embodiments, the
applications, and examples
described herein are given to illustrate the principles of the invention and
should not be construed as
limiting its scope. Those familiar with the art can yet envision, alter, and
use the methods and
systems of this invention in various situations and for many other
applications. Various
modifications to the specific embodiments could be introduced by those skilled
in the art without
departing from the scope and spirit of the invention as set forth in the
following claims.
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