Latent semantic analysis for application in a question answer system

• US 9,135,240 B2
• Filed: 02/12/2013
• Issued: 09/15/2015
• 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:
  US13/765,323
• Publication Number:
  US 20140229161A1
• Time in Patent Office: 945 Days
• Field of Search: 704/3, 704/4, 704/7, 704/9, 704/10
• US Class Current: 1/1
• 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 method for estimating similarity between concepts, 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 a programmed processor device is configured to perform said receiving, creating and deriving.

2. The method as claimed in claim 1, wherein said propagating a vector of a concept against said graph structure includes averaging the LSA vectors neighboring said concept.

3. The method as claimed in claim 1, wherein said corpus of text documents includes a knowledgebase (KB), said representative graph structure being generated from ontologies found in the KB.

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

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

7. The method as claimed in claim 5, wherein said undirected graph is derived by collapsing each of different relations between concepts in the knowledge base.

8. The method as claimed in claim 5, 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;

9. The method as claimed in claim 8, 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.