Query and document topic category transition analysis system and method and query expansion-based information retrieval system and method

• US 8,452,798 B2
• Filed: 02/17/2010
• Issued: 05/28/2013
• Est. Priority Date: 03/26/2009
• Status: Active Grant

• Current Assignee: Korea Advanced Institute of Science and Technology
• Original Assignee: Korea Advanced Institute of Science and Technology
• Inventors: Myaeng, Sung Hyon, Jung, Yu Chul, Kim, Kyung Min
• Primary Examiner(s): Ehichioya, Fred I
• Assistant Examiner(s): MACKES, KRIS E
• Application Number:
  US12/706,826
• Publication Number:
  US 20110047161A1
• Time in Patent Office: 1,196 Days
• Field of Search: 707/768
• US Class Current: 707/768
• CPC Class Codes: G06F 16/3338 Query expansion

Abstract

An information retrieval system and method, and more particularly, a query and document topic category transition analysis system and method in which a query topic category of a query input from a user as an information retrieval keyword and a document topic category of a document which a user regards as relevant and selects from information retrieval results are classified to analyze transition between the query topic category and the document topic category, and a query expansion-based information retrieval system and method using query and document topic category transition analysis in which a query input from a user is expanded using a topic category transition analysis result, and corresponding information or documents are retrieved using the expanded query are provided.

Claims

1. A method of analyzing transition between a query topic category and a document topic category, comprising:

determining a query topic category based on query and document text information with respect to a query input from a user and determining a document topic category based on query and document text information with respect to a document which a user selects from information retrieval results for the input query;

deriving a weight with respect to topic category transition between the classified query topic category and the classified document topic category;

generating a topic category transition map as a result of analyzing topic category transition between a user query and a relevant document based on the derived weight for the topic category transition;

generating corresponding documents as pseudo documents according to each topic category for the user query and the relevant document based on the generated topic category transition map; and

extracting at least one representative keyword from the generated pseudo documents and storing at least one representative keyword.

2. The method of claim 1, wherein in determining the query topic category and determining the document topic category, query and document text information is acquired using at least one external taxonomy, fine-grained hierarchical query topic category classification is performed, and fine-grained hierarchical document topic category classification is performed.

3. The method of claim 2, wherein the external taxonomy includes an open directory project (ODP) (also known as Directory Mozilla (DMOZ)) in which general interest of a user is included.

4. The method of claim 2, wherein the determining of the query topic category and the determining of the document topic category comprise:

generating a centroid vector using information expressing each topic category according to each topic category (a query topic category and a document topic category) stored in the external taxonomy;

computing similarity according to a corresponding query topic category for the input query and similarity according to a corresponding document topic category for the document, based on the generated centroid vector; and

determining a query topic category for the input query and a document topic category for the document, based on the computed similarity according to a corresponding query topic category and the computed similarity according to a corresponding document topic category.

5. The method of claim 4, wherein in generating the centroid vector, a centroid vector is generated using words included in corresponding snippet web documents belonging to each topic category.

6. The method of claim 1, wherein in deriving the weight, a category name and a relevance score for the classified query topic category are determined, a category name and a relevance score for the classified document topic category are determined, and a weight between a query topic category and a document topic category is derived based on the determined relevance scores.

7. The method of claim 1, wherein the generating of the topic category transition map comprises:

converting the derived weight for the topic category transition into a transition probability value that a certain query topic category will transition to a certain document topic category or a transition probability value that a certain query will transition to a certain document topic category; and

generating a topic category transition map as a result of analyzing topic category transition between a user query and a relevant document based on the converted query or document topic category transition probability value.

8. The method of claim 7, wherein the transition probability value that a certain query topic category will transition to a certain document topic category is computed by dividing a transition weight from a current query topic category to a certain document topic category by all transition weights derived from a current query topic category.

9. The method of claim 7, wherein the transition probability value that a certain query will transition to a certain document topic category is computed by adding all of probability values that each of a predetermined number of query topic categories as which a query can be classified will transition to a certain document topic category.

10. The method of claim 1, wherein in extracting the at least one representative keyword, a method of ranking representative keywords in documents collected according to each topic category is used.

11. A query expansion-based information retrieval method using query and document topic category transition analysis, comprising:

in a state in which a topic category transition map is generated as a result of analyzing topic category transition between a user query and a relevant document, and corresponding documents are generated as pseudo documents according to each topic category for the user query and the relevant document, determining a corresponding query topic category based on query and document text information for an input query input from a user;

allocating a relevant document topic category for the classified query topic category based on the topic category transition map;

ranking representative keywords for the query topic category and the relevant document topic category based on the pseudo documents;

expanding the input query using the ranked representative keywords; and

retrieving corresponding documents using the expanded query.

12. The method of claim 11, further comprising providing a user with the retrieved documents according to a ranking of the representative keywords.

13. The method of claim 11, wherein in ranking the representative keywords, a representative keyword list according to a ranking is generated by including at least one document topic category having relevance for the allocated query topic category.

14. The method of claim 11, wherein in expanding the input query, the input query and the ranked representative keywords are synthesized based on simple merging or a synthesis weight.