http://www.bibsonomy.org/burst/user/iswc2007/searchBibSonomy BuRST Feed for /user/iswc2007/search2008-10-07T19:16:49+02:00http://www.bibsonomy.org/bibtex/2e8c8cebd7906f804fc25316b782f0d0f/iswc2007iswc20072007-11-07T19:13:58+01:00semantic 2007 web_service semantic_web keyword software_agent ontology human-computer_interaction search information_extraction research_06 application_software iswc natural_language_processing interpretation <span style="color:#555555;">Thanh <a href="http://www.bibsonomy.org/author/Tran">Tran</a> und Philipp <a href="http://www.bibsonomy.org/author/Cimiano">Cimiano</a> und Sebastian <a href="http://www.bibsonomy.org/author/Rudolph">Rudolph</a> und Rudi <a href="http://www.bibsonomy.org/author/Studer">Studer</a> </span><em>Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, </em><em>Volume4825vonLNCS, </em><em>Seite519--532. </em><em>Berlin, Heidelberg, </em><em>Springer Verlag, </em><em>November2007. </em>Wed Nov 07 19:13:58 CET 2007Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember519--532LNCSOntology-based Interpretation of Keywords for Semantic Search48252007semantic 2007 web_service semantic_web keyword software_agent ontology human-computer_interaction search information_extraction research_06 application_software iswc natural_language_processing interpretation Current information retrieval (IR) approaches do not formally capture the explicit meaning of a keyword query but provide a comfortable way for the user to specify information needs on the basis of keywords. Ontology-based approaches allow for sophisticated semantic search but impose a query syntax more difficult to handle. In this paper, we present an approach for translating keyword queries to DL conjunctive queries using background knowledge available in ontologies. We present an implementation which shows that this interpretation of keywords can then be used for both exploration of asserted knowledge and for a semantics-based declarative query answering process. We also present an evaluation of our system and a discussion of the limitations of the approach with respect to our underlying assumptions which directly points to issues for future work.http://www.bibsonomy.org/bibtex/252943a6298169f5a552bffbbee352937/iswc2007iswc20072007-11-07T19:13:58+01:00social search research_06 iswc personalization 2007 tagging web bookmarking <span style="color:#555555;">Michael <a href="http://www.bibsonomy.org/author/Noll">Noll</a> und Christoph <a href="http://www.bibsonomy.org/author/Meinel">Meinel</a> </span><em>Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, </em><em>Volume4825vonLNCS, </em><em>Seite365--378. </em><em>Berlin, Heidelberg, </em><em>Springer Verlag, </em><em>November2007. </em>Wed Nov 07 19:13:58 CET 2007Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember365--378LNCSWeb search personalization via social bookmarking and tagging48252007social search research_06 iswc personalization 2007 tagging web bookmarking In this paper, we present a new approach to web search personalization based on user collaboration and sharing of information about web documents. The proposed personalization technique separates data collection and user profiling from the information system whose contents and indexed documents are being searched for, i.e. the search engines, and uses social bookmarking and tagging to re-rank web search results. It is independent of the search engine being used, so users are free to choose the one they prefer, even if their favorite search engine does not natively support personalization. We show how to design and implement such a system in practice and investigate its feasibility and usefulness with large sets of real-word data and a user study.http://www.bibsonomy.org/bibtex/2ce56748004a2f073a3b016b4e7a4e54a/iswc2007iswc20072007-11-07T19:13:58+01:00semantic application_software query ontology_(computer_science) 2007 iswc semantic_web search research_06 <span style="color:#555555;">Qi <a href="http://www.bibsonomy.org/author/Zhou">Zhou</a> und Chong <a href="http://www.bibsonomy.org/author/Wang">Wang</a> und Miao <a href="http://www.bibsonomy.org/author/Xiong">Xiong</a> und Haofen <a href="http://www.bibsonomy.org/author/Wang">Wang</a> und Yong <a href="http://www.bibsonomy.org/author/Yu">Yu</a> </span><em>Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, </em><em>Volume4825vonLNCS, </em><em>Seite687--700. </em><em>Berlin, Heidelberg, </em><em>Springer Verlag, </em><em>November2007. </em>Wed Nov 07 19:13:58 CET 2007Berlin, HeidelbergProceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Koreahttp://data.semanticweb.org/conference/iswc-aswc/2007/proceedingsNovember687--700LNCSSPARK: Adapting Keyword Query to Semantic Search48252007semantic application_software query ontology_(computer_science) 2007 iswc semantic_web search research_06 Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named ‘SPARK’ has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.