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Integrating multiple windows and document features for expert finding.

Zhu, Jianhan; Song, Dawei; R�ger, Stefan


Jianhan Zhu

Dawei Song

Stefan R�ger


Expert finding is a key task in enterprise search and has recently attracted lots of attention from both research and industry communities. Given a search topic, a prominent existing approach is to apply some information retrieval (IR) system to retrieve top ranking documents, which will then be used to derive associations between experts and the search topic based on cooccurrences. However, we argue that expert finding is more sensitive to multiple levels of associations and document features that current expert finding systems insufficiently address, including (a) multiple levels of associations between experts and search topics, (b) document internal structure, and (c) document authority. We propose a novel approach that integrates the above-mentioned three aspects as well as a query expansion technique in a two-stage model for expert finding. A systematic evaluation is conducted on TREC collections to test the performance of our approach as well as the effects of multiple windows, document features, and query expansion. These experimental results show that query expansion can dramatically improve expert finding performance with statistical significance. For three well-known IR models with or without query expansion, document internal structures help improve a single window-based approach but without statistical significance, while our novel multiple window-based approach can significantly improve the performance of a single window-based approach both with and without document internal structures.


ZHU, J., SONG, D. and RUGER, S. 2009. Integrating multiple windows and document features for expert finding. Journal of the Association for Information Science and Technology [online], 60(4), pages 694-715. Available from:

Journal Article Type Article
Acceptance Date Nov 13, 2008
Online Publication Date Mar 11, 2009
Publication Date Apr 30, 2009
Deposit Date Jun 4, 2009
Publicly Available Date Jun 4, 2009
Journal Journal of the American Society for Information Science and Technology
Print ISSN 1532-2882
Electronic ISSN 1532-2890
Publisher Association for Information Science and Technology (ASIS&T)
Peer Reviewed Peer Reviewed
Volume 60
Issue 4
Pages 694-715
Keywords Subject experts; Information retrieval models; Text mining; Document schemas; Query expansion; Expert finding; Enterprise search
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