Knowledge graph based search system

• US 20120158633A1
• Filed: 02/24/2012
• Published: 06/21/2012
• Est. Priority Date: 12/10/2002
• Status: Abandoned Application

• Current Assignee: Square Halt Solutions Limited Liability Company (Intellectual Ventures LLC)
• Original Assignee: Square Halt Solutions Limited Liability Company (Intellectual Ventures LLC)
• Inventors: Eder, Jeffrey Scott
• Application Number:
  US13/404,109
• Publication Number:
  US 20120158633A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

Methods, computer program products and systems for developing and implementing a Knowledge Based Search System for an entity. Entity related data are analyzed as required to develop an entity knowledge and one or more knowledge graphs. The knowledge graphs are used to support the retrieval of relevant search results.

Claims

1. A non-transitory computer program product, tangibly embodied in a machine readable storage device, comprising one or more instructions that, when executed, cause at least one processor to perform knowledge based search operations comprising:

aggregate, prepare and store a plurality of data, a plurality of definitions and two relevance criteria where said data and definitions comprise at least one entity, one entity function and one or more entity function measures where said data and definitions are stored in one or more categories,

transform said data into an entity knowledge by learning from the data,

use the relevance criteria and the entity knowledge to identify one or more of the categories as a source of data for any query,

receive a search request from the subject entity, and

provide a plurality of search results from the identified categories in response to said request by using two relevance measures to identify and prioritize the results where the entity knowledge comprises a graph.

2. The computer program product of claim 1, wherein the search results comprise a plurality of health related data.

3. The computer program product of claim 1, wherein the entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization.

4. The computer program product of claim 1, wherein the entity is selected from the group consisting of patient or patient-entity system.

5. The computer program product of claim 1, wherein the search request is received from a browser.

6. The computer program product of claim 1, wherein the two relevance criteria consist of a node depth and an impact cutoff and the two relevance measures are selected from the group consisting of ontology alignment measures, semantic alignment measures, cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores.

7. The computer program product of claim 1, wherein the search request comprises one or more keywords or a question where said search request is received from a natural language interface or an anticipated need for data automatically initiates the search request.

8. The computer program product of claim 1, wherein the at least one processor comprises at least one processor in a computer, at least one processor in a mobile access device or a combination thereof.

9. A knowledge based search system, comprising:

a computer with one or more processors having circuitry to execute instructions;

a storage device available to said processor with sequences of instructions stored therein, which when executed cause the one or more processors to;

aggregate, prepare and store a plurality of data, a plurality of definitions and two relevance criteria where said data and definitions comprise at least one entity, one entity function and one or more entity function measures where said data and definitions are stored in one or more categories,

transform said data into an entity knowledge by learning from the data,

use the relevance criteria and the entity knowledge to identify one or more of the categories as a source of data for any query,

receive a search request from the subject entity, and

provide a plurality of results from the identified categories in response to said query by using two relevance measures to identify and prioritize the results where the entity knowledge comprises a graph.

10. The system of claim 9, wherein the search results comprise a plurality of health related data.

11. The system of claim 9, wherein the entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization.

12. The system of claim 9, wherein the entity is selected from the group consisting of patient or patient-entity system.

13. The system of claim 9, wherein the search request is received from a browser.

14. The system of claim 9, wherein the two relevance criteria consist of a node depth and an impact cutoff and the two relevance measures are selected from the group consisting of ontology alignment measures, semantic alignment measures, cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores.

15. The system of claim 9, wherein the search request comprises one or more keywords or a question where said search request is received from a natural language interface or an anticipated need for data automatically initiates the search request.

16. The system of claim 9, wherein the at least one processor comprises at least one processor in a computer, at least one processor in a mobile access device or a combination thereof.

17. A knowledge based search method, comprising:

using a computer to;

aggregate, prepare and store a plurality of data, a plurality of definitions and two relevance criteria where said data and definitions comprise at least one entity, one entity function and one or more entity function measures where said data and definitions are stored in one or more categories,

transform said data into an entity knowledge by learning from the data,

use the relevance criteria and the entity knowledge to identify one or more of the categories as a source of data for any query,

receive a search request from the subject entity, and

provide a plurality of results from the identified categories in response to said query by using two relevance measures to identify and prioritize the results where the entity knowledge comprises a graph.

18. The system of claim 17, wherein the entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization.

19. The system of claim 17, wherein the entity is selected from the group consisting of patient or patient-entity system.

20. The system of claim 17, wherein the two relevance criteria consist of a node depth and an impact cutoff and the two relevance measures are selected from the group consisting of ontology alignment measures, semantic alignment measures, cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores.