User interface for summarizing the relevance of a document to a query

Patent metadata

Field	Value
Publication number	US-9734196-B2
Application number	US-201514644811-A
Country	US
Kind code	B2
Filing date	Mar 11, 2015
Priority date	Jul 14, 2014
Publication date	Aug 15, 2017
Grant date	Aug 15, 2017

Abstract

According to an aspect, summarizing relevance of a document to a conceptual query includes receiving the conceptual query, accessing concepts extracted from the document, and computing a degree to which the conceptual query is related to each of the extracted concepts. The computing is responsive to a metric that measures a relevance between the concepts in the conceptual query and the extracted concepts. The method also includes creating a summary by selecting a threshold number of the concepts having a greatest degree of relation to the conceptual query, and outputting the summary including the selected threshold number of concepts.

First claim

What is claimed is:

1. A method for summarizing relevance of a document to a conceptual query, the method comprising:
receiving the conceptual query, the conceptual query comprising one or more concepts in a concept graph;
accessing concepts extracted from the document, the concepts extracted from the document based on one or more differential tests performed on portions of text in the document and at least a subset of concepts in the concept graph;
computing a degree to which the conceptual query is related to each of the extracted concepts, the computing responsive to a metric that measures a relevance between the one or more concepts in the conceptual query and the each of the extracted concepts, the computing comprising using a transition probability matrix to perform a weighting of each link in the concept graph that connects the one or more concepts in the conceptual query to the extracted concepts;
creating a summary by selecting a threshold number of the extracted concepts having a greatest degree of relation to the conceptual query; and
outputting the summary including the selected threshold number of extracted concepts.

2. The method of claim 1 , wherein at least one concept in the summary is not one of the one or more concepts in the conceptual query.

3. The method of claim 1 , wherein the computing further comprises iterating a Markov chain derived from the concept graph.

4. The method of claim 1 , wherein the metric utilizes paths in the concept graph connecting the concepts in the conceptual query to each of the extracted concepts.

5. The method of claim 1 , wherein the summary is output to a user interface.

6. The method of claim 5 , wherein the each of the selected threshold number of concepts in the summary is associated with a hyperlink via the user interface.

7. The method of claim 6 , further comprising:
receiving an indication that a user has selected the hyperlink from the user interface; and
based on receiving the indication, outputting a new list of summaries related to the concepts in the hyperlink.

8. The method of claim 5 , where the relevance of an extracted concept to a conceptual query is summarized by at least one of changing a font size of the extracted concept and changing a color of the extracted concept.

9. The method of claim 5 , wherein any concept in the selected number of concepts that also forms part of the conceptual query is not displayed by the user interface.

10. The method of claim 1 , wherein the summary includes excerpts of text from documents highlighting extracted concepts that have a degree of relevance to the conceptual query.

11. The method of claim 1 , where the computing is performed at indexing time for conceptual queries comprised of a single concept, and at least a subset of results of the computing are stored in an explanations index.

12. The method of claim 11 , where the creating a summary retrieves the explanations index.

13. The method of claim 1 , wherein the conceptual query includes at least two concepts, and wherein the creating a summary is responsive to a degree of relation between each of the concepts in the conceptual query and each of the extracted concepts.

14. A method for summarizing relevance of documents to a conceptual query, the method comprising:
receiving the conceptual query, the conceptual query comprising a listing of one or more concepts in a concept graph;
accessing extracted concepts for each of the documents, the extracted concepts extracted from the each of the documents based on one or more differential tests performed on portions of text in the each of the documents and at least a subset of concepts in the concept graph;
computing a degree to which each of the documents are related to one another, the computing responsive to a metric that measures a relevance between the extracted concepts in one document and extracted concepts in another document, the computing comprising using a transition probability matrix to perform a weighting of each link in the concept graph that connects the extracted concepts in one document to the extracted concepts in another document;
assigning the documents to one or more groups based on the computing, wherein a pair of documents having a first score that specifies a degree of relation is more likely to be in the same group than a pair of documents having a second score specifying a degree of relation that is lower than the first score; and
outputting results of the assigning including the one or more groups of documents.

15. The method of claim 14 , where the computing the degree of relation among each of the documents is accomplished as part of a clustering algorithm.

16. The method of claim 14 , wherein the metric utilizes paths in the concept graph connecting the extracted concepts in one document to the extracted concepts in another document.

Assignees

• IBM

Inventors

• Franceschini Michele M
• Lastras-Montano Luis A
• Soares Livio B
• Wegman Mark N

Classifications

• G06F17/30424 Primary

  Physics · mapped topic

• G06F17/30684

  Physics · mapped topic

• G06F16/245 Primary

  Query processing · CPC title

• G06F16/3344 Primary

  using natural language analysis · CPC title