@inproceedings{
Title = {SPARK: Adapting Keyword Query to Semantic Search},
Booktitle = {6th International and 2nd Asian Semantic Web Conference (ISWC2007+ASWC2007)},
Year = {2007},
Month = {November},
Pages = {687--700},
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.},
Url = {http://data.semanticweb.org/conference/iswc-aswc/2007/tracks/research/papers/687}
}