Application of Spreading Activation Techniques in InformationRetrieval
Artificial Intelligence Review
Foundations of statistical natural language processing
Foundations of statistical natural language processing
WWW '03 Proceedings of the 12th international conference on World Wide Web
Structural ambiguity and lexical relations
Computational Linguistics - Special issue on using large corpora: I
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APCCM '04 Proceedings of the first Asian-Pacific conference on Conceptual modelling - Volume 31
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A hybrid approach for searching in the semantic web
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A survey and classification of semantic search approaches
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ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
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ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
OTTHO: On the Tip of My THOught
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Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Editorial: Querying linked data graphs using semantic relatedness: A vocabulary independent approach
Data & Knowledge Engineering
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The Semantic Desktop is a means to support users in Personal Information Management (PIM). It provides an excellent test bed for Semantic Web technology: resources (e. g., persons, projects, messages, documents) are distributed amongst multiple systems, ontologies are used to link and annotate them. Finding information is a core element in PIM. For the end user, the search interface has to be intuitive to use, natural language queries provide a simple mean to express requests. State of the art semantic search engines focus on fact retrieval or on semantic document retrieval. We combine both approaches to search the Semantic Desktop exploiting all available information. Our semantic search engine, built on semantic teleporting and spreading activation, is able to answer natural language queries with facts, e. g., a specific phone number, and/or relevant documents. We evaluated our approach on ESWC 2007 data in comparison with Google site search.