Expert Search Engine Using Co-Diffusion
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 3
Abstract
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 web pages and thousands of names are considered. It has mainly two challenging issues: 1) web pages could be of varying quality and full of noises; 2) The expertise evidences scattered in web pages are usually vague and ambiguous. We propose to leverage the large amount of cooccurrence information to assess relevance and reputation of a person name for a query topic. The co-occurrence structure is modeled using a hyper graph, 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.
Authors and Affiliations
Sayali Nikam, Trupti Raikar, Priyanka Tile, Prof. S. N. Bhadane
Keywords
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