Modeling document features for expert finding
http://data.open.ac.uk/oro/25887
is a Article , Academic article

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Dataset Open Research Online
At The 17th ACM Conference on Information and Knowledge Management (CIKM'08)
Date 2008
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Status Peer reviewed
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  • http://data.open.ac.uk/oro/document/21964
  • http://data.open.ac.uk/oro/document/21965
  • http://data.open.ac.uk/oro/document/22090
  • http://data.open.ac.uk/oro/document/24963
Abstract We argue that expert finding is sensitive to multiple document features in an organization, and therefore, can benefit from the incorporation of these document features. We propose a unified language model, which integrates multiple document features, namely, multiple levels of associations, PageRank, indegree, internal document structure, and URL length. Our experiments on two TREC Enterprise Track collections, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios.
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Type
Label Zhu, Jianhan ; Song, Dawei ; Rüger, Stefan and Huang, Xiangji (2008). Modeling document features for expert finding. In: Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08, p. 1421.
Same as 1458082.1458312
Title Modeling document features for expert finding