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Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition

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Knowledge Graphs and Semantic Web (KGSWC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1232))

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Abstract

A booming amount of information is continuously added to the Internet as structured and unstructured data, feeding knowledge bases such as DBpedia and Wikidata with billions of statements describing millions of entities. The aim of Question Answering systems is to allow lay users to access such data using natural language without needing to write formal queries. However, users often submit questions that are complex and require a certain level of abstraction and reasoning to decompose them into basic graph patterns. In this short paper, we explore the use of architectures based on Neural Machine Translation called Neural SPARQL Machines to learn pattern compositions. We show that sequence-to-sequence models are a viable and promising option to transform long utterances into complex SPARQL queries.

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Notes

  1. 1.

    https://github.com/LiberAI/NSpM/wiki/Compositionality.

  2. 2.

    The metadata can be fetched from http://mappings.dbpedia.org/server/ontology/classes/.

  3. 3.

    https://github.com/paulhoule/telepath/wiki/SubjectiveEye3D.

  4. 4.

    Retrieved on 19/10/2020 from https://dbpedia.org/sparql.

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Authors and Affiliations

  1. AKSW, University of Leipzig, Leipzig, Germany

    Tommaso Soru & Edgard Marx

  2. Leipzig University of Applied Sciences, Leipzig, Germany

    Edgard Marx

  3. Liber AI Research, London, UK

    Anand Panchbhai, Tommaso Soru & Edgard Marx

Authors
  1. Anand Panchbhai
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  2. Tommaso Soru
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  3. Edgard Marx
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Corresponding author

Correspondence to Tommaso Soru .

Editor information

Editors and Affiliations

  1. Tinámica, Madrid, Spain

    Boris Villazón-Terrazas

  2. Tamaulipas Autonomous University, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodríguez

  3. Tamaulipas Autonomous University, Ciudad Victoria, Mexico

    Sanju M. Tiwari

  4. VIT Bhopal University, Bhopal, India

    Shishir K. Shandilya

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Panchbhai, A., Soru, T., Marx, E. (2020). Exploring Sequence-to-Sequence Models for SPARQL Pattern Composition. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S.M., Shandilya, S.K. (eds) Knowledge Graphs and Semantic Web. KGSWC 2020. Communications in Computer and Information Science, vol 1232. Springer, Cham. https://doi.org/10.1007/978-3-030-65384-2_12

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  • DOI: https://doi.org/10.1007/978-3-030-65384-2_12

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