Abstract
With the rapid development of Semantic Web, more and more RDF repositories, such as Linking Open Data (LOD), are available on the web. Generally, there are two services provided for exploring those RDF repositories, one is the keyword lookup, and the other is the SPARQL endpoint. Most users choose the lookup service, and millions of web logs have been recorded. Although, users expect to submit more expressive queries than keyword lookup, the complexity of SPARQL undoubtedly scared users away. This paper proposes a method of SPARQL query recommendation for exploring RDF repositories. By analyzing web logs of the lookup service, our method extracts the user access patterns, which will be used to recommend SPARQL queries. We implement our method based on Zhishi.me, a Chinese RDF repository with about 150 million triples as well as over one-year web logs. We believe the proposed method will further facilitate the SPARQL query research.
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