Knowledge canvassing using a knowledge graph and a question and answer system
First Claim
1. 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 implement a cognitive system for processing a knowledge canvassing request, wherein the computer readable program causes the data processing system to:
- receive, by the cognitive system, a request specifying at least one entity of interest from an originator of the request, wherein the cognitive system comprises a request classification engine, a factoid QA system pipeline, and a knowledge canvassing pipeline;
analyze, by the cognitive system, the request to extract one or more features of the request;
determine, by the request classification engine, whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on a comparison of the one or more extracted features against one or more classification rules or patterns;
in response to determining that the request is a targeted natural language question, route, by the request classification engine, the request to the factoid QA system pipeline which processes the request as a natural language question using natural language processing (NLP) mechanisms;
in response to determining that the request is a knowledge canvassing request, route, by the request classification engine, the request to the knowledge canvassing pipeline and process, by the knowledge canvassing pipeline, the request by identifying entities represented in a knowledge graph data structure as being related to the at least one entity of interest to suggest other areas of potential interest to the originator of the request; and
output, by the cognitive system, results of the processing of the request to the originator of the request,wherein processing the request comprises;
identifying, by the cognitive system, entities in the request;
performing, by relationship search logic within the knowledge canvassing pipeline, a search of the knowledge graph to find corresponding nodes to the identified entities and identifying related nodes in the knowledge graph that have links connecting the identified entities with the related nodes representing related entities;
analyzing, by passage relevancy scoring logic within the knowledge canvassing pipeline, evidence passages of a corpus of documents that are associated with the related nodes to determine whether there is support for relationships between the identified entities and the related entities;
selecting, by candidate relationship generation logic within the knowledge canvassing pipeline, a set of candidate relationships between identified entities and related entities based on candidate relationship selection criteria;
performing, by independent relevancy scoring logic within the knowledge canvassing pipeline, analysis of the set of candidate relationships that is context independent;
identifying, by context dependent relevancy scoring logic within the knowledge canvassing pipeline, portions of the corpus of documents where entities of the set of candidate relationships are mentioned and evaluates a context of the portions of the corpus of documents to determine a context dependent metric to associate with each candidate relationship;
determining, by final merging and ranking logic within the knowledge canvassing pipeline, a ranked listing of the set of candidate relationships based on context dependent and context independent metrics; and
retrieving, by evidence passage retrieval logic within the knowledge canvassing pipeline, evidential passages from the corpus of documents that reference the entities in the ranked set of candidate relationships.
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Accused Products
Abstract
Mechanisms for processing a knowledge canvassing request 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.
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Citations
18 Claims
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1. 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 implement a cognitive system for processing a knowledge canvassing request, wherein the computer readable program causes the data processing system to:
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receive, by the cognitive system, a request specifying at least one entity of interest from an originator of the request, wherein the cognitive system comprises a request classification engine, a factoid QA system pipeline, and a knowledge canvassing pipeline; analyze, by the cognitive system, the request to extract one or more features of the request; determine, by the request classification engine, whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on a comparison of the one or more extracted features against one or more classification rules or patterns; in response to determining that the request is a targeted natural language question, route, by the request classification engine, the request to the factoid QA system pipeline which processes the request as a natural language question using natural language processing (NLP) mechanisms; in response to determining that the request is a knowledge canvassing request, route, by the request classification engine, the request to the knowledge canvassing pipeline and process, by the knowledge canvassing pipeline, the request by identifying entities represented in a knowledge graph data structure as being related to the at least one entity of interest to suggest other areas of potential interest to the originator of the request; and output, by the cognitive system, results of the processing of the request to the originator of the request, wherein processing the request comprises; identifying, by the cognitive system, entities in the request; performing, by relationship search logic within the knowledge canvassing pipeline, a search of the knowledge graph to find corresponding nodes to the identified entities and identifying related nodes in the knowledge graph that have links connecting the identified entities with the related nodes representing related entities; analyzing, by passage relevancy scoring logic within the knowledge canvassing pipeline, evidence passages of a corpus of documents that are associated with the related nodes to determine whether there is support for relationships between the identified entities and the related entities; selecting, by candidate relationship generation logic within the knowledge canvassing pipeline, a set of candidate relationships between identified entities and related entities based on candidate relationship selection criteria; performing, by independent relevancy scoring logic within the knowledge canvassing pipeline, analysis of the set of candidate relationships that is context independent; identifying, by context dependent relevancy scoring logic within the knowledge canvassing pipeline, portions of the corpus of documents where entities of the set of candidate relationships are mentioned and evaluates a context of the portions of the corpus of documents to determine a context dependent metric to associate with each candidate relationship; determining, by final merging and ranking logic within the knowledge canvassing pipeline, a ranked listing of the set of candidate relationships based on context dependent and context independent metrics; and retrieving, by evidence passage retrieval logic within the knowledge canvassing pipeline, evidential passages from the corpus of documents that reference the entities in the ranked set of candidate relationships. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. An apparatus comprising:
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a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to implement a cognitive system for processing a knowledge canvassing request, wherein the instructions causes the processor to; receive, by the cognitive system, a request specifying at least one entity of interest from an originator of the request, wherein the cognitive system comprises a request classification engine, a factoid QA system pipeline, and a knowledge canvassing pipeline; analyze, by the cognitive system, the request to extract one or more features of the request; determine, by the request classification engine, whether the request is a targeted natural language question to be answered or a knowledge canvassing request, based on a comparison of the one or more extracted features against one or more classification rules or patterns; in response to determining that the request is a targeted natural language question, route, by the request classification engine, the request to the factoid QA system pipeline which processes the request as a natural language question using natural language processing (NLP) mechanisms; in response to determining that the request is a knowledge canvassing request, route, by the request classification engine, the request to the knowledge canvassing pipeline and process, by the knowledge canvassing pipeline, the request by identifying entities represented in a knowledge graph data structure as being related to the at least one entity of interest to suggest other areas of potential interest to the originator of the request; and output, by the cognitive system, results of the processing of the request to the originator of the request, wherein processing the request comprises; identifying, by the cognitive system, entities in the request; performing, by relationship search logic within the knowledge canvassing pipeline, a search of the knowledge graph to find corresponding nodes to the identified entities and identifying related nodes in the knowledge graph that have links connecting the identified entities with the related nodes representing related entities; analyzing, by passage relevancy scoring logic within the knowledge canvassing pipeline, evidence passages of a corpus of documents that are associated with the related nodes to determine whether there is support for relationships between the identified entities and the related entities; selecting, by candidate relationship generation logic within the knowledge canvassing pipeline, a set of candidate relationships between identified entities and related entities based on candidate relationship selection criteria; performing, by independent relevancy scoring logic within the knowledge canvassing pipeline, analysis of the set of candidate relationships that is context independent; identifying, by context dependent relevancy scoring logic within the knowledge canvassing pipeline, portions of the corpus of documents where entities of the set of candidate relationships are mentioned and evaluates a context of the portions of the corpus of documents to determine a context dependent metric to associate with each candidate relationship; determining, by final merging and ranking logic within the knowledge canvassing pipeline, a ranked listing of the set of candidate relationships based on context dependent and context independent metrics; and retrieving, by evidence passage retrieval logic within the knowledge canvassing pipeline, evidential passages from the corpus of documents that reference the entities in the ranked set of candidate relationships. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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Specification