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DOI: 10.4230/OASIcs.LDK.2019.1
URN: urn:nbn:de:0030-drops-103651
URL: https://drops.dagstuhl.de/opus/volltexte/2019/10365/
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Adamou, Alessandro ; Allocca, Carlo ; d'Aquin, Mathieu ; Motta, Enrico

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

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OASIcs-LDK-2019-1.pdf (0.6 MB)


Abstract

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.

BibTeX - Entry

@InProceedings{adamou_et_al:OASIcs:2019:10365,
  author =	{Alessandro Adamou and Carlo Allocca and Mathieu d'Aquin and Enrico Motta},
  title =	{{SPARQL Query Recommendation by Example: Assessing the Impact of Structural Analysis on Star-Shaped Queries}},
  booktitle =	{2nd Conference on Language, Data and Knowledge (LDK 2019)},
  pages =	{1:1--1:8},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-105-4},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{70},
  editor =	{Maria Eskevich and Gerard de Melo and Christian F{\"a}th and John P. McCrae and Paul Buitelaar and Christian Chiarcos and Bettina Klimek and Milan Dojchinovski},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/10365},
  URN =		{urn:nbn:de:0030-drops-103651},
  doi =		{10.4230/OASIcs.LDK.2019.1},
  annote =	{Keywords: SPARQL, query recommendation, query structure, dataset profiling}
}

Keywords: SPARQL, query recommendation, query structure, dataset profiling
Seminar: 2nd Conference on Language, Data and Knowledge (LDK 2019)
Issue Date: 2019
Date of publication: 20.05.2019


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