TITLE

An approach to query expansion based on Bayesian belief network and relevance feedback

AUTHOR(S)
Li Ming
PUB. DATE
March 2012
SOURCE
International Journal of Digital Content Technology & its Applic;Mar2012, Vol. 6 Issue 4, p237
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Besides the full text query, documents can be retrieved by attributes of the document in enterprise document retrieval. To improve the retrieval performance, an approach to expand the full text query with attribute query is proposed. Firstly, full text search is conducted. The retrieved documents the relevance of which has been judged by the user are the data source to construct the attribute query. The possibility of the match between the keywords and the attributes is derived from the Bayesian belief network. The match with maximum possibility is used as the attribute query to expand the full text query. Afterwards, the documents are searched again with the expand query. The results of the experiment show that the proposed approach improves more efficiency than the traditional query expansion methods.
ACCESSION #
76361771

 

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