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Integrating multiple document features in language models for expert finding

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Abstract

We argue that expert finding is sensitive to multiple document features in an organizational intranet. These document features include multiple levels of associations between experts and a query topic from sentence, paragraph, up to document levels, document authority information such as the PageRank, indegree, and URL length of documents, and internal document structures that indicate the experts’ relationship with the content of documents. Our assumption is that expert finding can largely benefit from the incorporation of these document features. However, existing language modeling approaches for expert finding have not sufficiently taken into account these document features. We propose a novel language modeling approach, which integrates multiple document features, for expert finding. Our experiments on two large scale TREC Enterprise Track datasets, 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. Our main contribution is to develop an effective formal method for modeling multiple document features in expert finding, and conduct a systematic investigation of their effects. It is worth noting that our novel approach achieves better results in terms of MAP than previous language model based approaches and the best automatic runs in both the TREC2006 and TREC2007 expert search tasks, respectively.

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Authors and Affiliations

  1. Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    Jianhan Zhu

  2. School of Information Technology, York University, Toronto, M3J 1P3, Canada

    Xiangji Huang

  3. School of Computing, The Robert Gordon University, Aberdeen, AB25 1HG, UK

    Dawei Song

  4. Knowledge Media Institute, The Open University, Milton Keynes, MK7 6AA, UK

    Stefan Rüger

Authors
  1. Jianhan Zhu
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  2. Xiangji Huang
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  3. Dawei Song
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  4. Stefan Rüger
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Correspondence to Jianhan Zhu.

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Zhu, J., Huang, X., Song, D. et al. Integrating multiple document features in language models for expert finding. Knowl Inf Syst 23, 29–54 (2010). https://doi.org/10.1007/s10115-009-0202-6

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