%0 Conference Paper %1 Zhou/2007/SPARK: %A Zhou, Qi %A Wang, Chong %A Xiong, Miao %A Wang, Haofen %A Yu, Yong %B Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea %C Berlin, Heidelberg %D 2007 %E Aberer, Karl %E Choi, Key-Sun %E Noy, Natasha %E Allemang, Dean %E Lee, Kyung-Il %E Nixon, Lyndon J B %E Golbeck, Jennifer %E Mika, Peter %E Maynard, Diana %E Schreiber, Guus %E Cudré-Mauroux, Philippe %I Springer Verlag %K 2007 application_software iswc ontology_(computer_science) query research_06 search semantic semantic_web %P 687--700 %T SPARK: Adapting Keyword Query to Semantic Search %U http://iswc2007.semanticweb.org/papers/687.pdf %V 4825 %X 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.

@inproceedings{Zhou/2007/SPARK:, abstract = {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.}, added-at = {2007-11-07T19:13:58.000+0100}, address = {Berlin, Heidelberg}, author = {Zhou, Qi and Wang, Chong and Xiong, Miao and Wang, Haofen and Yu, Yong}, biburl = {https://www.bibsonomy.org/bibtex/2ce56748004a2f073a3b016b4e7a4e54a/iswc2007}, booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea}, crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings}, editor = {Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon J B and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Schreiber, Guus and Cudré-Mauroux, Philippe}, interhash = {352810110bf2fc1a820d89c7c7c39980}, intrahash = {ce56748004a2f073a3b016b4e7a4e54a}, keywords = {2007 application_software iswc ontology_(computer_science) query research_06 search semantic semantic_web}, month = {November}, pages = {687--700}, publisher = {Springer Verlag}, series = {LNCS}, timestamp = {2007-11-07T19:20:54.000+0100}, title = {SPARK: Adapting Keyword Query to Semantic Search}, url = {http://iswc2007.semanticweb.org/papers/687.pdf}, volume = 4825, year = 2007 }