Latent semantic analysis for application in a question answer system

• US 9,020,810 B2
• Filed: 08/14/2013
• Issued: 04/28/2015
• Est. Priority Date: 02/12/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Gliozzo, Alfio M.
• Primary Examiner(s): GUERRA-ERAZO, EDGAR X
• Application Number:
  US13/966,902
• Publication Number:
  US 20140229163A1
• Time in Patent Office: 622 Days
• Field of Search: 704/3, 704/4, 704/7, 704/9, 704/10
• US Class Current: 704/9
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A system and method that improves obtaining similarity measure between concepts based on Latent Semantic Analysis by taking onto account graph structure derived from the knowledge bases by using a vector propagation algorithm, in the context domain, such as a medical domain. Concepts contained in a corpus of documents are expressed in a graph wherein each node is a concept and edges between node express relation between concepts weighted by the number of semantic relations determined from the corpus. A vector of neighbors is created and assigned to each concept, thereby providing an improved similarity measure between documents, i.e., corpus and query against corpus.

Claims

1. A system for estimating similarity between concepts comprising:

one or more content sources providing content;

a programmed processor device for coupling to said content sources and configured to;

receive a set of concepts related to a corpus of text documents in a content source;

create a representative graph structure having graph nodes each representing a latent semantic analysis (LSA) vector associated with a concept, and a node having one or more graph edges, each graph edge representing a strength of a relation between concepts based on an ontology; and

derive, for a concept, a new or modified vector represented by a node in the graph by propagating the LSA vectors against said graph structure, said new or modified vector representing a modified estimated similarity between concepts.

2. The system as claimed in claim 1, wherein to propagate a vector of a concept against said graph structure, said processor device is further configured to:

average the LSA vectors neighboring said concept.

3. The system as claimed in claim 1, wherein a content source includes a knowledgebase (KB), said representative graph structure being generated from ontologies found in the KB.

4. The system as claimed in claim 3, wherein said knowledgebase includes a medical knowledgebase.

5. The system as claimed in claim 3, wherein said created graph structure is an undirected graph generated from ontologies found in the KB, said created graph showing the relations of concepts weighted by a number of relations, wherein each edge between related concepts is a weight representing the number of relations.

6. The system as claimed in claim 5, wherein a semantic relation includes an ISA relation.

7. The system as claimed in claim 5, wherein to derive said undirected graph, said processor device is further configured to:

collapse each of different relations between concepts in the knowledge base.

8. The system as claimed in claim 5, wherein to propagate a vector of a concept against said graph structure, said processor device is further configured to:

for a known LSA concept vector c_i, apply a formula against data of said undirected graph, said formula comprising;

9. The system as claimed in claim 8, wherein to apply the vector propagation formula, said processor device is further configured to:

given a concept vector “

c”

,

identifying the vectors for the concept in graph;

computing an average of those identified vectors, and

replacing the original concept vector c with new vector average.

10. A computer program product for estimating similarity between concepts, the computer program product comprising a tangible, non-transitory storage medium readable by a processing circuit and storing instructions run by the processing circuit for performing a method, the method comprising:

receiving a set of concepts related to a corpus of text documents;

creating a representative graph structure having graph nodes each representing a latent semantic analysis (LSA) vector associated with a concept, and a node having one or more graph edges, each graph edge representing a strength of a relation between concepts based on an ontology; and

deriving, for a concept, a new or modified vector represented by a node in the graph by propagating the LSA vectors against said graph structure, said new or modified vector representing a modified estimated similarity between concepts, wherein the storage medium readable by a processing circuit is not only a propagating signal.

11. The computer program product as claimed in claim 10, wherein said propagating a vector of a concept against said graph structure includes averaging the LSA vectors neighboring said concept.

12. The computer program product as claimed in claim 10, wherein said corpus of text documents includes a knowledgebase (KB), said representative graph structure being generated from ontologies found in the KB.

13. The computer program product as claimed in claim 12, wherein said created graph structure is an undirected graph generated from ontologies found in the KB, said created graph showing the relations of concepts weighted by a number of relations, wherein each edge between related concepts is a weight representing the number of relations.

14. The computer program product as claimed in claim 13, wherein said vector propagating comprises:

for a known LSA concept vector c_i, applying a formula against data of said undirected graph, said formula comprising;

15. The computer program product as claimed in claim 14, wherein the applying said vector propagation formula comprises:

given a concept vector “

c”

,

identifying the vectors for the concept in graph;

computing an average of those identified vectors, and

replacing the original concept vector c with new vector average.