Falcons: searching and browsing entities on the semantic web
Proceedings of the 17th international conference on World Wide Web
Sindice.com: a document-oriented lookup index for open linked data
International Journal of Metadata, Semantics and Ontologies
A Generative Theory of Relevance
A Generative Theory of Relevance
Exploiting web search engines to search structured databases
Proceedings of the 18th international conference on World wide web
From capturing semantics to semantic search: a virtuous cycle
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Expressive and flexible access to web-extracted data: a keyword-based structured query language
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Discovering meaning on the go in large heterogenous data
Artificial Intelligence Review
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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 feedback.