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.
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These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y. (2007). SPARK: Adapting Keyword Query to Semantic Search. In: Aberer, K., et al. The Semantic Web. ISWC ASWC 2007 2007. Lecture Notes in Computer Science, vol 4825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76298-0_50
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DOI: https://doi.org/10.1007/978-3-540-76298-0_50
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