Knowledge Canvassing Using a Knowledge Graph and a Question and Answer System

• US 20160378851A1
• Filed: 06/25/2015
• Published: 12/29/2016
• Est. Priority Date: 06/25/2015
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Sirotkovic, Jadran, Merdivan, Erinc, Riendeau, John A., Whitley, Michael D., Zhang, Le
• Granted Patent: US 10,586,156 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 40/247 Thesauruses; Synonyms
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

Mechanisms are provided for processing a knowledge canvassing request. The mechanisms receive a request specifying an entity of interest from an originator of the request and analyze the request to extract a feature of the request. The mechanisms determine whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on the extracted feature. In response to determining that the request is a knowledge canvassing request, the mechanisms process the request by identifying entities represented in a knowledge graph data structure as being related to the entity of interest. The mechanisms output results of the processing of the request to the originator of the request.

Claims

1-10. -10. (canceled)

11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to:

receive a request specifying at least one entity of interest from an originator of the request;

analyze the request to extract one or more features of the request;

determine whether the request is a targeted natural language question to be answered by the data processing system or a knowledge canvassing request, based on the one or more extracted features;

in response to determining that the request is a knowledge canvassing request, process the request by identifying entities represented in a knowledge graph data structure as being related to the at least one entity of interest; and

output results of the processing of the request to the originator of the request.

12. The computer program product of claim 11, wherein the data processing system comprises a factoid question and answer (QA) system pipeline and a knowledge canvassing pipeline, and wherein the computer readable program further causes the data processing system to:

in response to determining that the request is a targeted natural language question, route the request to the factoid QA system pipeline which processes the request as a natural language question using natural language processing (NLP) mechanisms; and

in response to determining that the request is a knowledge canvassing request, route the request to the knowledge canvassing pipeline which performs the processing of the request by identifying entities represented in the knowledge graph data structure as being related to the at least one entity of interest.

13. The computer program product of claim 11, wherein the knowledge graph data structure comprises a plurality of nodes representing different entities identified in a corpus of information ingested by the data processing system, and edges between nodes representing relationships between the entities corresponding to the nodes as identified by analyzing a context of references to the entities in the corpus of information.

14. The computer program product of claim 13, wherein the computer readable program further causes the data processing system to process the request by identifying entities represented in the knowledge graph data structure as being related to the at least one entity of interest at least by identifying at least one node in the knowledge graph corresponding to the at least one entity of interest and related entities connected to the at least one node by one or more edges in the knowledge graph data structure.

15. The computer program product of claim 14, wherein the computer readable program further causes the data processing system to process the request at least by ranking the related entities by scoring each related entity in accordance with a relatedness metric associated with edges connecting the related entity to the at least one entity of interest, and wherein the computer readable program further causes the data processing system to output results of the processing at least by outputting a ranked listing of the related entities based on the ranking of the related entities.

16. The computer program product of claim 15, wherein the relatedness metric is an inverse document frequency (IDF) metric representing a degree of rarity of the relationship between the related entity and the entity of interest in the corpus of information.

17. The computer program product of claim 15, wherein the ranking of the related entities is performed in accordance with a domain specific ranking criteria indicating a criteria that is of greater desirability in the context of the domain.

18. The computer program product of claim 15, wherein the computer readable program further causes the data processing system to receive a user input selecting a related entity in the ranked listing of related entities to be a new entity of interest, and repeating the analyze, determine, process, and output operations with the new entity of interest being the entity of interest associated with a new request to identify related entities.

19. The computer program product of claim 15, wherein scoring each related entity comprises generating, for the related entity, a context independent score and a context dependent score, and calculating a score for the related entity based on a combination of the context independent score and the context dependent score.

20. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to output results of the processing of the request to the originator of the request at least by outputting results comprising a listing of one or more related entities and, for each related entity of the one or more related entities, a portion of at least one text passage from a corpus of information in which a relationship of the related entity with the entity of interest is referenced.

21. An apparatus comprising:

a processor; and

a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to;

receive a request specifying at least one entity of interest from an originator of the request;

analyze the request to extract one or more features of the request;

determine whether the request is a targeted natural language question to be answered by the data processing system or a knowledge canvassing request, based on the one or more extracted features;

in response to determining that the request is a knowledge canvassing request, process the request by identifying entities represented in a knowledge graph data structure as being related to the at least one entity of interest; and

output results of the processing of the request to the originator of the request.