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Co-Occurrence-Based Diffusion for Expert Search on the Web
May 2013 (vol. 25 no. 5)
pp. 1001-1014
Ziyu Guan, University of California, Santa Barbara, Santa Barbara
Gengxin Miao, University of California, Santa Barbara, Santa Barbara
Russell McLoughlin, Lawrence Livermore National Laboratory, Livermore
Xifeng Yan, University of California, Santa Barbara, Santa Barbara
Deng Cai, Zhejiang Univerisity, Hangzhou
Expert search has been studied in different contexts, e.g., enterprises, academic communities. We examine a general expert search problem: searching experts on the web, where millions of webpages and thousands of names are considered. It has mainly two challenging issues: 1) webpages could be of varying quality and full of noises; 2) The expertise evidences scattered in webpages are usually vague and ambiguous. We propose to leverage the large amount of co-occurrence information to assess relevance and reputation of a person name for a query topic. The co-occurrence structure is modeled using a hypergraph, on which a heat diffusion based ranking algorithm is proposed. Query keywords are regarded as heat sources, and a person name which has strong connection with the query (i.e., frequently co-occur with query keywords and co-occur with other names related to query keywords) will receive most of the heat, thus being ranked high. Experiments on the ClueWeb09 web collection show that our algorithm is effective for retrieving experts and outperforms baseline algorithms significantly. This work would be regarded as one step toward addressing the more general entity search problem without sophisticated NLP techniques.
Index Terms:
Web pages,Search problems,Noise,Computational modeling,Space heating,Conductivity,diffusion,Expert search,web mining,hypergraph
Citation:
Ziyu Guan, Gengxin Miao, Russell McLoughlin, Xifeng Yan, Deng Cai, "Co-Occurrence-Based Diffusion for Expert Search on the Web," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 5, pp. 1001-1014, May 2013, doi:10.1109/TKDE.2012.49
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