Relevance Feedback between Web Search and the Semantic Web

Harry Halpin, Victor Lavrenko

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We investigate the possibility of using structured data to improve search over unstructured documents. In particular, we use relevance feedback to create a ‘virtuous cycle’ between structured data from the Semantic Web and web-pages from the hypertext Web. Previous approaches have generally considered searching over the Semantic Web and hypertext Web to be entirely disparate, indexing and searching over different domains. Our novel approach is to use relevance feedback from hypertext Web results to improve Semantic Web search, and results from the Semantic Web to improve the retrieval of hypertext Web data. In both cases, our evaluation is based on certain kinds of informational queries (abstract concepts, people, and places) selected from a real-life query log and checked by human judges. We show our relevance model-based system is better than the performance of real-world search engines for both hypertext and Semantic Web search, and we also investigate Semantic Web inference and pseudo-relevance feed-back.
Original languageEnglish
Title of host publicationIJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011
Number of pages6
Publication statusPublished - Jul 2011


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