Methods and devices for customizing knowledge representation systems

• US 10,248,669 B2
• Filed: 04/11/2017
• Issued: 04/02/2019
• Est. Priority Date: 06/22/2010
• Status: Active Grant

• Current Assignee: Primal Fusion Inc.
• Original Assignee: Primal Fusion Inc.
• Inventors: Sweeney, Peter, Ilyas, Ihab Francis
• Primary Examiner(s): Holmes, Michael B
• Application Number:
  US15/484,679
• Publication Number:
  US 20170220607A1
• Time in Patent Office: 721 Days
• CPC Class Codes: G06F 16/211 Schema design and management
• CPC Class Codes: G06F 16/212 with details for data modelling support
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

Techniques for analyzing and synthesizing complex knowledge representations (KRs) may utilize an atomic knowledge representation model including both an elemental data structure and knowledge processing rules stored as machine-readable data and/or programming instructions. One or more of the knowledge processing rules may be applied to analyze an input complex KR to deconstruct its complex concepts and/or concept relationships to elemental concepts and/or concept relationships to be included in the elemental data structure. One or more of the knowledge processing rules may be applied to synthesize an output complex KR from the stored elemental data structure in accordance with context information. Methods of populating an elemental data structure and methods of synthesizing complex KRs from the elemental data structure may depend on user models, context information, preference information, and/or preference models.

Claims

1. A method comprising:

using at least one processor to perform;

providing, to a first set of one or more users, access to a first semantic network;

providing, to a second set of one or more users, access to a second semantic network, wherein at least a second subset of the second semantic network has a same topology as at least a first subset of the first semantic network; and

in response to identifying information corresponding to the second set of one or more users, modifying the second semantic network based, at least in part, on the information corresponding to the second set of one or more users by adding to the second semantic network one or more first elements not included in the first semantic network and/or removing from the second semantic network one or more second elements included in the first semantic network,

wherein each of the one or more first and second elements comprises a concept or a relationship, and

wherein the first and second semantic networks are implemented as computer-readable data structures, and wherein modifying the second semantic network is performed by one or more processors executing computer-readable instructions.

2. The method of claim 1, wherein the modifying comprises:

using the information corresponding to the second set of one or more users to determine whether the one or more first elements are relevant to the second set of one or more users; and

in response to determining that the one or more first elements are relevant to the second set of one or more users, adding the one or more first elements to the second semantic network.

3. The method of claim 2, wherein modifying the second semantic network further comprises:

using the information corresponding to the second set of one or more users to determine whether the one or more second elements are relevant to the second set of one or more users; and

in response to determining that the one or more second elements are not relevant to the second set of one or more users, removing the one or more second elements from the second semantic network.

4. The method of claim 1, wherein the modifying comprises:

using the information corresponding to the second set of one or more users to determine whether the one or more first elements are associated with the second set of one or more users; and

in response to determining that the one or more first elements are associated with the second set of one or more users, adding the one or more first elements to the second semantic network.

5. The method of claim 4, wherein the modifying further comprises:

using the information corresponding to the second set of one or more users to determine whether the one or more second elements are associated with the second set of one or more users; and

in response to determining that the one or more second elements are not associated with the second set of one or more users, removing the one or more second elements from the second semantic network.

6. The method of claim 4, wherein using the information corresponding to the second set of one or more users to determine whether the one or more first elements are associated with the second set of one more users comprises:

estimating what portion of the second set of one or more users is associated with the one or more first elements; and

determining whether the portion of the second set of one or more users associated with the one or more first elements exceeds a threshold portion.

7. The method of claim 1, wherein the method further comprises:

using information corresponding to a plurality of users to determine what portion of the plurality of users is associated with one or more third elements, wherein each of the one or more third elements comprises a concept or a relationship; and

in response to determining that the portion of the plurality of users associated with the one or more third elements exceeds a threshold portion, adding the one or more third elements to the first and second semantic networks.

8. The method of claim 7, wherein the method further comprises:

in response to determining that the portion of the plurality of users associated with the one or more third elements is less than a threshold portion, removing the one or more third elements from the first and second semantic networks.

9. The method of claim 1, further comprising:

in response to receiving a search query provided by the second set of one or more users, identifying search results matching the search query, wherein identifying the search results comprises searching the second semantic network; and

causing the search results to be presented to the second set of one or more users.

10. The method of claim 9, wherein the information corresponding to the second set of one or more users comprises information regarding the search query provided by the second set of one or more users and/or information regarding an interaction of the second set of one or more users with the search results obtained by searching the second semantic network.

11. The method of claim 10, wherein the interaction of the second set of one or more users with the search results comprises at least one member of the second set of one or more users selecting a subset of the search results.

12. The method of claim 9, wherein the information corresponding to the second set of one or more users comprises at least one type of information selected from the group consisting of query information, demographic information, biographical information, employment information, familial information, preference information, interest information, financial information, geo-spatial information, browsing history information, website profile information, or social networking profile information associated with the at least one user.

13. The method of claim 10, further comprising feeding back the information corresponding to the second set of one or more users from a search result output of a knowledge representation system to an information analysis input of the knowledge representation system.

14. The method of claim 11, further comprising applying one or more rules to deconstruct the information corresponding to the second set of one or more users to obtain the one or more first elements.

15. The method of claim 1, wherein the at least a first subset of the first semantic network and the at least a second subset of the second semantic network which have the same topology are implemented as a same computer-readable data structure.

16. The method of claim 1, wherein prior to modifying the second semantic network, the first and second semantic networks are identical.

17. A knowledge representation system, comprising:

one or more processors; and

a memory unit configured to store instructions which, when executed by the one or more processors, cause the one or more processors to perform a method comprising;

providing, to a first set of one or more users, access to a first semantic network;

providing, to a second set of one or more users, access to a second semantic network, wherein at least a second subset of the second semantic network has a same topology as at least a first subset of the first semantic network; and

in response to identifying information corresponding to the second set of one or more users, modifying the second semantic network based, at least in part, on the information corresponding to the second set of one or more users by adding to the second semantic network one or more first elements not included in the first semantic network and/or removing from the second semantic network one or more second elements included in the first semantic network,

wherein each of the one or more first and second elements comprises a concept or a relationship, and

wherein the first and second semantic networks are implemented as computer-readable data structures, and wherein modifying the second semantic network is performed by one or more processors executing computer-readable instructions.

18. The knowledge representation system of claim 17, wherein the modifying comprises:

using the information corresponding to the second set of one or more users to determine whether the one or more first elements are relevant to the second set of one or more users; and

in response to determining that the one or more first elements are relevant to the second set of one or more users, adding the one or more first element to the second semantic network.

19. The knowledge representation system of claim 17, wherein the modifying comprises:

using the information corresponding to the second set of one or more users to determine whether the one or more first elements are associated with the second set of one or more users; and

in response to determining that the one or more first elements are associated with the second set of one or more users, adding the one or more first elements to the second semantic network.

20. The knowledge representation system of claim 17, wherein the method further comprises:

in response to receiving a search query provided by the second set of one or more users, identifying search results matching the search query, wherein identifying the search results comprises searching the second semantic network; and

causing the search results to be presented to the second set of one or more users.

21. A non-transitory computer-readable storage medium storing processor-executable instructions which, when executed by one or more processors, cause the processors to perform a method comprising:

providing, to a first set of one or more users, access to a first semantic network;

providing, to a second set of one or more users, access to a second semantic network, wherein at least a second subset of the second semantic network has a same topology as at least a first subset of the first semantic network; and

in response to identifying information corresponding to the second set of one or more users, modifying the second semantic network based, at least in part, on the information corresponding to the second set of one or more users by adding to the second semantic network one or more first elements not included in the first semantic network and/or removing from the second semantic network one or more second elements included in the first semantic network,

wherein each of the one or more first and second elements comprises a concept or a relationship, and

wherein the first and second semantic networks are implemented as computer-readable data structures, and wherein modifying the second semantic network is performed by one or more processors executing computer-readable instructions.