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An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs

Finding Patterns for Human Associations in DBpedia

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10024))

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Abstract

Efficient usage of the knowledge provided by the Linked Data community is often hindered by the need for domain experts to formulate the right SPARQL queries to answer questions. For new questions they have to decide which datasets are suitable and in which terminology and modelling style to phrase the SPARQL query.

In this work we present an evolutionary algorithm to help with this challenging task. Given a training list of source-target node-pair examples our algorithm can learn patterns (SPARQL queries) from a SPARQL endpoint. The learned patterns can be visualised to form the basis for further investigation, or they can be used to predict target nodes for new source nodes.

Amongst others, we apply our algorithm to a dataset of several hundred human associations (such as “circle - square”) to find patterns for them in DBpedia. We show the scalability of the algorithm by running it against a SPARQL endpoint loaded with \(> 7.9\) billion triples. Further, we use the resulting SPARQL queries to mimic human associations with a Mean Average Precision (MAP) of \(39.9\,\%\) and a Recall@10 of \(63.9\,\%\).

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Notes

  1. 1.

    http://lod-cloud.net/.

  2. 2.

    https://www.w3.org/TR/rdf-sparql-query/.

  3. 3.

    For our purpose G is a set of RDF triples, typically accessible via a given SPARQL endpoint.

  4. 4.

    http://dbpedia.org/sparql.

  5. 5.

    https://www.w3.org/TR/sparql11-query/#BasicGraphPatterns.

  6. 6.

    https://w3id.org/associations.

  7. 7.

    Also available at https://w3id.org/associations.

  8. 8.

    Most notably: DBpedia 2015-04 (en, de), Freebase, Yago, Wikidata, GeoNames, DBLP, Wordnet and BabelNet.

  9. 9.

    The full dataset is available at https://w3id.org/associations.

  10. 10.

    https://w3id.org/associations.

  11. 11.

    We don’t provide Precision@k, as it degenerates to \(\text {Recall@k}/k\) due to the fact that we only have 1 relevant target per result of any \((s_t, t_t)\).

  12. 12.

    https://w3id.org/associations.

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Authors and Affiliations

  1. Computer Science Department, University of Kaiserslautern, Kaiserslautern, Germany

    Jörn Hees, Rouven Bauer, Joachim Folz, Damian Borth & Andreas Dengel

  2. Knowledge Management Department, DFKI GmbH, Kaiserslautern, Germany

    Jörn Hees, Rouven Bauer, Joachim Folz, Damian Borth & Andreas Dengel

Authors
  1. Jörn Hees
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  2. Rouven Bauer
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  3. Joachim Folz
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  4. Damian Borth
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  5. Andreas Dengel
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Correspondence to Jörn Hees .

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Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Eva Blomqvist

  2. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  3. University of Bologna, Bologna, Italy

    Francesco Poggi

  4. University of Bologna, Bologna, Italy

    Fabio Vitali

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Hees, J., Bauer, R., Folz, J., Borth, D., Dengel, A. (2016). An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_22

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  • DOI: https://doi.org/10.1007/978-3-319-49004-5_22

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