Ontological Subjects Of A Universe And Knowledge Processing Thereof

• US 20150169746A1
• Filed: 02/07/2015
• Published: 06/18/2015
• Est. Priority Date: 07/24/2008
• Status: Active Grant

• Current Assignee: Hamid Hatami-Hanza
• Original Assignee: Hamid Hatami-Hanza
• Inventors: Hatami-Hanza, Hamid
• Granted Patent: US 9,679,030 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/22 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/25 Integrating or interfacing systems involving database management systems
• CPC Class Codes: G06F 16/358 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

The present invention discloses methods, systems, and tools for knowledge processing by building various data structures corresponding to uncovered informational data such as values of association strengths or significance measures and maps of ontological subjects of compositions or one or more content accompanying a request for service by a user. In one embodiment of the invention the method assigns and calculates an ontological subject association strength/value measures and spectrums to each composition or ontological subject. The resulting data, spectrums, and the adjacency matrix of the map are used to evaluate the merits of the compositions in the context of reference universes. It is also used as a research guiding tool for knowledge discovery or automatically generating high value compositions or new or less known knowledge about the ontological subjects of the universe. The invention serves knowledge seekers, knowledge creators, intelligent machines or robots, inventors, discoverer, as well as general public by assisting and guiding users to assess their work, identify their unknowns, optimize their research trajectory, and provide higher quality content. The method and system, thereby, is instrumental in increasing the speed and efficiency of knowledge acquisition, machine learnings, autonomous decision making, navigations, discovery, retrieval, as well as faster learning and problem solving.

Claims

1. A method of generating one or more data structures corresponding to novel association strengths of ontological subjects of a composition comprising:

building, using one or more data processing or computing devices, said one or more data processing or computing devices having a singular or compound processing speed of one thousand million or larger than one thousand million instructions per second, one or more data structures corresponding to co-occurrences of a plurality of ontological subjects of the composition within predefined proximities in the composition,

building one or more data structure corresponding to association strength spectrum vectors for a plurality of ontological subjects of the composition according to least one association strength measure, wherein each association spectrum vector indicates association strength between one of the ontological subjects of the composition with one or more ontological subjects of the composition,

calculating, using one or more data processing or computing devices, a novel association strength value between a pair of ontological subjects as a function of association strength spectrum vectors and the co-occurrence number of the pair of ontological subjects, and

storing one or more of said one or more data structures corresponding to the novel association strengths of a plurality of ontological subjects of the composition in one or more non-transitory storage media and/or processing the ontological subjects based on their novel association strength value with each other.

2. The method of claim 1, wherein the composition of ontological subject comprises one or more of:

a. one or more news content,

b. a picture,

c. a video,

d. a DNA string code,

e. a graph and/or a data array representative a graph,

f. a computer readable code,

g. a multimedia content,

h. a textual content,

i. a binary string,

j. a digital signal,

k. an electrical signal,

l. a data file,

m. one or more web pages,

n. a given corpus, and

o. a body of knowledge.

3. The method of claim 1 wherein the novel association strength value is used to suggest a research topic.

4. The method of claim 1, further comprising using the data of association strength spectrum vectors or the novel association strength value to do at least one of followings:

a. identifying or finding related ontological subjects;

b. identifying or finding most important subjects related to another ontological subject,

c. identifying or finding indirect or novel relation of two or more ontological subjects,

d. composing a question,

e. calling a function,

f. anomaly detection,

g. composing a relevant composition to one or more ontological subjects, and

h. scoring partitions of the composition.

5. The method of claim 1 wherein the data of association strength spectrum vectors or the novel association strength value is used to build a neural network with weight between nodes based on the association strength of ontological subjects.

6. The method of claim 1 wherein the data of association strength spectrum vectors or the novel association strength value is used to visually or graphically show the relations of ontological subjects of the composition with a visual shape wherein the shape is built using a function of association strengths of the ontological subjects of the composition with each other.

7. A method of generating one or more data structures corresponding to indirect association strengths of ontological subjects of a composition comprising:

building, using one or more data processing or computing devices, said one or more data processing or computing devices having a singular or compound processing speed of one thousand million or larger than one thousand million instructions per second, one or more data structures corresponding to co-occurrences of a plurality of ontological subjects of the composition within predefined proximities in the composition,

building one or more data structure using data of said one or more data structures corresponding to said co-occurrences, corresponding to association strength spectrum vectors for a plurality of ontological subjects of the composition according to least one association strength measure, wherein each association spectrum vector indicates association strength between one of the ontological subjects of the composition with one or more ontological subjects of the composition,

calculating, using one or more data processing or computing devices, an indirect association strength value between a pair of ontological subjects as a function of the association strength spectrum vectors and invert of their co-occurrence number of the pair of ontological subjects, and

storing one or more of said one or more data structures corresponding to the indirect association strengths of a plurality of ontological subjects of the composition in one or more non-transitory storage media and/or processing the ontological subjects based on their indirect association strength value with each other.

8. The method of claim 7, wherein the composition of ontological subject contains one or more of:

a. one or more news content,

b. a picture,

c. a video,

d. a DNA string code,

e. a graph and/or a data array representative a graph,

f. a computer readable code,

g. a multimedia content,

h. a textual content,

i. a binary string,

j. a digital signal,

k. an electrical signal,

l. a data file,

m. one or more web pages,

n. a given corpus, and

o. a body of knowledge.

9. The method of claim 7 wherein the indirect association strength value is used to suggest a research topic.

10. The method of claim 7, further comprising using the data of association strength spectrum vectors or the indirect association strength value to do at least one of followings:

a. identifying or finding related ontological subjects;

b. identifying or finding most important subjects related to another ontological subject,

c. identifying or finding indirect or novel relation of two or more ontological subjects,

d. composing a question,

e. calling a function,

f. anomaly detection,

g. composing a relevant composition to one or more ontological subjects, and

h. scoring partitions of the composition.

11. The method of claim 7 wherein the data of association strength spectrum vectors or the indirect association strength value is used to build a neural network with weight between nodes based on the association strength of ontological subjects.

12. The method of claim 7 wherein the data of association strength spectrum vectors or the indirect association strength value is used to visually or graphically show the relations of ontological subjects of the composition with a visual shape wherein the shape is built using a function of association strengths of the ontological subjects of the composition.

13. A method of investigating a composition of ontological subjects comprising:

partitioning the composition, using one or more data processing or computing devices, said one or more data processing or computing devices having a singular or compound processing speed of one thousand million or larger than one thousand million instructions per second, into a plurality of sets of partitions or ontological subjects, wherein each set of the plurality of sets of partitions or ontological subjects is assigned with a predefined ontological subject order,

building, using one or more data processing or computing devices,

one or more data structures corresponding to participation of ontological subjects of a set of ontological subjects, assigned with a first predefined order, in ontological subjects, from a set of ontological subjects of the composition assigned with a second predefined order, or co-occurrences of ontological subjects, from a set of ontological subjects assigned with a first predefined order, in ontological subjects from a set of ontological subjects of the composition assigned with a second predefined order,

building one or more data structures corresponding to values of one or more association strength measures between one or more pairs of ontological subjects of the first predefined order, using data of the co-occurrences of said one or more pairs of ontological subjects of the composition,

processing the ontological subject using the one or more data structures corresponding to the values of one or more association strength measures of the one or more pairs of ontological subjects of the composition.

14. The method of claim 13, wherein the composition of ontological subject contains one or more of:

a. one or more news content,

b. a picture,

c. a video,

d. a DNA string code,

e. a graph and/or a data array representative a graph,

f. a computer readable code,

g. a multimedia content,

h. a textual content,

i. a binary string,

j. a digital signal,

k. an electrical signal,

l. a data file,

m. one or more web pages,

n. a given corpus, and

o. a body of knowledge.

15. The method of claim 13 further comprising:

building, one or more data structure representing an ontological subject map using the data of one more data structures corresponding to the values of at least one of the one or more association strength measures between the ontological subjects of the composition by arranging the ontological subjects based on the values of their association strengths with each other and a set of predefined rules;

storing one or more of said one or more data structures representing the ontological subject map in one or more non-transitory storage media.

16. The method of claim 13, further comprising using the data of one or more association strength measures to do at least one of followings:

a. finding related ontological subjects;

b. identifying most important subjects related to another ontological subject,

c. identifying or finding indirect relation of two or more ontological subjects,

d. composing a question,

e. calling a function,

f. feature extraction,

g. calcification of compositions,

h. composing a relevant composition to one or more ontological subjects, and

i. scoring partitions of the composition.

17. The method of claim 13 wherein the data of one or more association strength measures is used to build a neural network with weight between nodes based on the association strength of ontological subjects.

18. The method of claim 13 wherein the data of one or more association strength measures is used to visually or graphically show the relations of ontological subjects of the composition with a visual shape wherein the shape is built using a function of association strengths of the ontological subjects of the composition.

19. The method claim 16 further comprising data communication devices/units to send and receive one or more said data structures over a data network.