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Category-Based Query Modeling for Entity Search

Authors :
Balog, K.
Bron, M.
de Rijke, M.
Gurrin, C.
He, Y.
Kazai, G.
Kruschwitz, U.
Little, S.
Roelleke, T.
Rüger, S.
van Rijsbergen, K.
Information and Language Processing Syst (IVI, FNWI)
Source :
Lecture Notes in Computer Science ISBN: 9783642122743, ECIR, Advances in Information Retrieval: 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010: proceedings, 319-331, STARTPAGE=319;ENDPAGE=331;TITLE=Advances in Information Retrieval
Publication Year :
2010
Publisher :
Springer Berlin Heidelberg, 2010.

Abstract

Users often search for entities instead of documents and in this setting are willing to provide extra input, in addition to a query, such as category information and example entities. We propose a general probabilistic framework for entity search to evaluate and provide insight in the many ways of using these types of input for query modeling. We focus on the use of category information and show the advantage of a category-based representation over a term-based representation, and also demonstrate the effectiveness of category-based expansion using example entities. Our best performing model shows very competitive performance on the INEX-XER entity ranking and list completion tasks.

Details

ISBN :
978-3-642-12274-3
ISBNs :
9783642122743
Database :
OpenAIRE
Journal :
Lecture Notes in Computer Science ISBN: 9783642122743, ECIR, Advances in Information Retrieval: 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010: proceedings, 319-331, STARTPAGE=319;ENDPAGE=331;TITLE=Advances in Information Retrieval
Accession number :
edsair.doi.dedup.....574844347abc7bdb789e6f09b91aa3d9