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SPARQL Query Recommendations by Example

  • Carlo AlloccaEmail author
  • Alessandro Adamou
  • Mathieu d’Aquin
  • Enrico Motta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9989)

Abstract

In this demo paper, a SPARQL Query Recommendation Tool (called SQUIRE) based on query reformulation is presented. Based on three steps, Generalization, Specialization and Evaluation, SQUIRE implements the logic of reformulating a SPARQL query that is satisfiable w.r.t a source RDF dataset, into others that are satisfiable w.r.t a target RDF dataset. In contrast with existing approaches, SQUIRE aims at recommending queries whose reformulations: (i) reflect as much as possible the same intended meaning, structure, type of results and result size as the original query and (ii) do not require to have a mapping between the two datasets. Based on a set of criteria to measure the similarity between the initial query and the recommended ones, SQUIRE demonstrates the feasibility of the underlying query reformulation process, ranks appropriately the recommended queries, and offers a valuable support for query recommendations over an unknown and unmapped target RDF dataset, not only assisting the user in learning the data model and content of an RDF dataset, but also supporting its use without requiring the user to have intrinsic knowledge of the data.

Notes

Acknowledgments

This work was supported by the MK:Smart project (OU Reference HGCK B4466).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Carlo Allocca
    • 1
    Email author
  • Alessandro Adamou
    • 1
  • Mathieu d’Aquin
    • 1
  • Enrico Motta
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK

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