NDLI: Using hierarchical document evaluation and weighted associations to find experts in enterprise corpora

Content Provider	IEEE Xplore Digital Library
Author	Wei Zhang Jianqing Ma Yiping Zhong
Copyright Year	2009
Description	Author affiliation: Department of Computer, Fudan University, handan Road,200433, Shanghai, China (Jianqing Ma; Yiping Zhong) \|\| School of Information Science and Engineering, Fudan University, handan Road,200433, Shanghai, China (Wei Zhang)
Abstract	An expert finding system enables individuals within a large organization to search for authoritative people in a certain area. Various approaches have been proposed to solve the problem of expert finding and most of them can be classified as either document model or candidate model. In this paper, we propose a novel strategy based on the document model to improve the performance of expert finding system. Since association building and document evaluation are of primary importance to document model, we intend to estimate the strength of association in a better manner than traditional methods by using the count of candidates' occurrences as a factor, improve the document evaluation by employing a hierarchical method which incorporates document structure. What's more, we employ two methods of query expansion based on pseudo relevance feedback and proximity constrains respectively to reformulate original topic to improve the accuracy of expert finding. Finally, we evaluate our methods on the Enterprise corpora provided by Text REtrieval Conference (TREC). Experimental results shows that our strategy brings substantial gains to the expert finding and delivers excellent performance.
Starting Page	657
Ending Page	661
File Size	101512
Page Count	5
File Format	PDF
ISBN	9781424447541
DOI	10.1109/ICICISYS.2009.5358088
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information science Databases Buildings Feedback Performance gain World Wide Web Information retrieval Knowledge management Personnel Data mining
Content Type	Text
Resource Type	Article

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