Facetedpedia: Dynamic generation of query-dependent faceted interfaces for Wikipedia
|Facetedpedia: Dynamic generation of query-dependent faceted interfaces for Wikipedia|
|Author(s)||Li C., Yan N., Roy S.B., Lisham L., Das G.|
|Published in||Proceedings of the 19th International Conference on World Wide Web, WWW '10|
|Keyword(s)||data exploration, faceted search, wikipedia (Extra: Category systems, Data exploration, Dynamic generation, Experimental evaluation, Faceted search, Folksonomies, Hyperlinks, Information discovery, Interface discovery, Internal structure, Keyword queries, Retrieval systems, Semantic information, Sheer size, User study, Wikipedia, Hypertext systems, Information retrieval, Navigation, World Wide Web)|
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Facetedpedia: Dynamic generation of query-dependent faceted interfaces for Wikipedia is a 2010 conference paper written in English by Li C., Yan N., Roy S.B., Lisham L., Das G. and published in Proceedings of the 19th International Conference on World Wide Web, WWW '10.
This paper proposes Facetedpedia, a faceted retrieval system for information discovery and exploration in Wikipedia. Given the set of Wikipedia articles resulting from a keyword query, Facetedpedia generates a faceted interface for navigating the result articles. Compared with other faceted retrieval systems, Facetedpedia is fully automatic and dynamic in both facet generation and hierarchy construction, and the facets are based on the rich semantic information from Wikipedia. The essence of our approach is to build upon the collaborative vocabulary in Wikipedia, more specifically the intensive internal structures (hyperlinks) and folksonomy (category system). Given the sheer size and complexity of this corpus, the space of possible choices of faceted interfaces is prohibitively large. We propose metrics for ranking individual facet hierarchies by user's navigational cost, and metrics for ranking interfaces (each with k facets) by both their average pairwise similarities and average navigational costs. We thus develop faceted interface discovery algorithms that optimize the ranking metrics. Our experimental evaluation and user study verify the effectiveness of the system.
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