Topic-driven web search result organization by leveraging Wikipedia semantic knowledge
|Topic-driven web search result organization by leveraging Wikipedia semantic knowledge|
|Author(s)||Han X., Zhao J.|
|Published in||International Conference on Information and Knowledge Management, Proceedings|
|Keyword(s)||Search result organization, Topic detection, Topic ranking, Topic-driven clustering (Extra: Empirical results, Search result organization, Search results, Semantic knowledge, Support vector regression models, Topic detection, Topic ranking, Topic-driven clustering, Web searches, Wikipedia, Clustering algorithms, Information retrieval, Regression analysis, Search engines, Semantic Web, Semantics, Websites, Knowledge management)|
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Topic-driven web search result organization by leveraging Wikipedia semantic knowledge is a 2010 conference paper written in English by Han X., Zhao J. and published in International Conference on Information and Knowledge Management, Proceedings.
Effective organization of web search results can greatly improve the utility of search engine and enhance the quality of search results. However, the organization of search results is difficult because the sub-topics of a query are usually not explicitly given. In this paper, we propose a novel topic-driven search result organization method, which can first detect the sub-topics of a query by finding the coherent Wikipedia concept groups from its search results; then organize these results using a topic-driven clustering algorithm; in the end we score and rank the topics using the support vector regression model. Empirical results show that our method can achieve competitive performance.
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