Cyc: toward programs with common sense
Communications of the ACM
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of a very large web search engine query log
ACM SIGIR Forum
A language modeling approach to information retrieval
A language modeling approach to information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
WWW '03 Proceedings of the 12th international conference on World Wide Web
Simple BM25 extension to multiple weighted fields
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey and classification of semantic search approaches
International Journal of Metadata, Semantics and Ontologies
Falcons: searching and browsing entities on the semantic web
Proceedings of the 17th international conference on World Wide Web
A Generative Theory of Relevance
A Generative Theory of Relevance
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
Repeatable and reliable search system evaluation using crowdsourcing
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Characterizing the semantic web on the web
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Journal of Biomedical Informatics
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We investigate the possibility of using Semantic Web data to improve hypertext Web search. In particular, we use relevance feedback to create a 'virtuous cycle' between data gathered from the Semantic Web of Linked Data and web-pages gathered from the hypertext Web. Previous approaches have generally considered the searching over the Semantic Web and hypertext Web to be entirely disparate, indexing, and searching over different domains. While relevance feedback has traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings over a single data-set. 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, an evaluation is performed 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 evaluate our work over a wide range of algorithms and options, and show it improves baseline performance on these queries for deployed systems as well, such as the Semantic Web Search engine FALCON-S and Yahoo! Web search. We further show that the use of Semantic Web inference seems to hurt performance, while the pseudo-relevance feedback increases performance in both cases, although not as much as actual relevance feedback. Lastly, our evaluation is the first rigorous 'Cranfield' evaluation of Semantic Web search.