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SPARQL Query Recommendation by Example: Assessing the Impact of Structural Analysis on Star-Shaped Queries


Alessandro Adamou, Carlo Allocca, Mathieu d'Aquin, Enrico Motta

Publication Type: 
Refereed Conference Meeting Proceeding
One of the existing query recommendation strategies for unknown datasets is “by example”, i.e. based on a query that the user already knows how to formulate on another dataset within a similar domain. In this paper we measure what contribution a structural analysis of the query and the datasets can bring to a recommendation strategy, to go alongside approaches that provide a semantic analysis. Here we concentrate on the case of star-shaped SPARQL queries over RDF datasets. The illustrated strategy performs a least general generalization on the given query, computes the specializations of it that are satisfiable by the target dataset, and organizes them into a graph. It then visits the graph to recommend first the reformulated queries that reflect the original query as closely as possible. This approach does not rely upon a semantic mapping between the two datasets. An implementation as part of the SQUIRE query recommendation library is discussed.
Conference Name: 
2nd Conference on Language, Data and Knowledge (LDK 2019)
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Research Group: 
National University of Ireland, Galway (NUIG)
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