Semantic Web Dog Food

Presented at: 6th International and 2nd Asian Semantic Web Conference (ISWC2007+ASWC2007)

by Qi Zhou, Chong Wang, Miao Xiong, Haofen Wang, Yong Yu

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.

SPARK: Adapting Keyword Query to Semantic Search was presented at this event.

Keywords: Application software, Ontology (computer science), Ontology (Computer Science), Semantic Web

Resource URI on the dog food server: http://data.semanticweb.org/conference/iswc-aswc/2007/tracks/research/papers/687
Same as: http://revyu.com/things/iswc-aswc-2007-research-paper-687-spark-adapting

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