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SPARQL Query Recommendations by Example

Allocca, Carlo; Adamou, Alessandro; d'Aquin, Mathieu and Motta, Enrico (2016). SPARQL Query Recommendations by Example. In: 13th ESWC 2016, 29th May - 2nd June 2016, Crete, Greece.

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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 rec- ommending 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.

Item Type: Conference or Workshop Item
Project Funding Details:
Funded Project NameProject IDFunding Body
MK:smartHGCK B4466The Open University (OU)
Keywords: SPARQL Query; Recommendation; Linked Open Data
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 46757
Depositing User: Carlo Allocca
Date Deposited: 26 Jul 2016 14:21
Last Modified: 21 Nov 2016 19:09
URI: http://oro.open.ac.uk/id/eprint/46757
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