SPARK: Adapting Keyword Query to Semantic Search

From semanticweb.org

A paper written by Yong Yu, Haofen Wang, Chong Wang, Miao Xiong and Qi Zhou. It was presented at the ISWC2007+ASWC2007.

[edit] 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.

A linked list of all papers is provided in the article on ISWC2007+ASWC2007 papers. This article has originally been created from the ISWC 2007/ASWC 2007 metadata.