%0 Book Section %1 citeulike:4039817 %A Zhou, Qi %A Wang, Chong %A Xiong, Miao %A Wang, Haofen %A Yu, Yong %D 2008 %J The Semantic Web %K aswc07, iswc07 %P 694--707 %R http://dx.doi.org/10.1007/978-3-540-76298-0\_50 %T SPARK: Adapting Keyword Query to Semantic Search %U http://dx.doi.org/10.1007/978-3-540-76298-0\_50 %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.

@incollection{citeulike:4039817, 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 = {2009-02-13T13:22:04.000+0100}, author = {Zhou, Qi and Wang, Chong and Xiong, Miao and Wang, Haofen and Yu, Yong}, biburl = {https://www.bibsonomy.org/bibtex/2ac63bc9707e9a17d07198faded492cfb/conchuir}, citeulike-article-id = {4039817}, doi = {http://dx.doi.org/10.1007/978-3-540-76298-0\_50}, interhash = {c68668cf40bf7fe01c5a6fa03b1d529a}, intrahash = {ac63bc9707e9a17d07198faded492cfb}, journal = {The Semantic Web}, keywords = {aswc07, iswc07}, pages = {694--707}, posted-at = {2009-02-12 18:11:52}, priority = {2}, timestamp = {2009-02-13T13:22:05.000+0100}, title = {SPARK: Adapting Keyword Query to Semantic Search}, url = {http://dx.doi.org/10.1007/978-3-540-76298-0\_50}, year = 2008 }