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DBSpark: A System for Natural Language to SPARQL Translation

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Research Challenges in Information Science: Information Science and the Connected World (RCIS 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 476))

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Abstract

Knowledge bases offer clear advantages when compared to traditional databases, mainly due to semantic connections and automated reasoning over large datasets. However, limited knowledge of the specialized knowledge base query language (SPARQL) makes it difficult for most users to freely access these resources. To solve this issue, we propose a question-answering system able to translate natural language questions into SPARQL queries. The presented method is a rule-based approach that integrates information regarding dependency and constituency parsing, WordNet and named entity recognition to capture the structural and semantic representation of the question. The proposed solution is able to handle a wide variety of question types (list, count, yes/no, wh-questions, questions involving rankings, ordinals, and/or superlatives). Moreover, all involved steps except the phrase mapping phase (in which properties and entities from the ontological model are mapped to words from the natural language question) are knowledge base independent. Tests performed over the QALD-9 question-answering dataset using the DBpedia knowledge base have shown that our system obtains state-of-the-art results and a very good time-performance balance.

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Notes

  1. 1.

    https://wordnet.princeton.edu/documentation/.

  2. 2.

    https://www.dbpedia.org/.

  3. 3.

    https://www.wikidata.org/wiki/Wikidata:Main_Page.

  4. 4.

    https://qald.aksw.org/.

  5. 5.

    https://spacy.io/.

  6. 6.

    https://pypi.org/project/word2number/.

  7. 7.

    https://pypi.org/project/geonamescache/.

  8. 8.

    https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yago-naga/patty.

  9. 9.

    https://pypi.org/project/sparqlwrapper/.

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Authors and Affiliations

  1. Faculty of Computer Science, Alexandru Ioan Cuza University, Iaşi, Romania

    Laura-Maria Cornei & Diana Trandabat

Authors
  1. Laura-Maria Cornei
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  2. Diana Trandabat
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Correspondence to Laura-Maria Cornei .

Editor information

Editors and Affiliations

  1. Université Paris 1 Panthéon-Sorbonne, Paris, France

    Selmin Nurcan

  2. University of Bergen, Bergen, Norway

    Andreas L. Opdahl

  3. University of Essex, Colchester, UK

    Haralambos Mouratidis

  4. Ionian University, Corfu, Greece

    Aggeliki Tsohou

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Cornei, LM., Trandabat, D. (2023). DBSpark: A System for Natural Language to SPARQL Translation. In: Nurcan, S., Opdahl, A.L., Mouratidis, H., Tsohou, A. (eds) Research Challenges in Information Science: Information Science and the Connected World. RCIS 2023. Lecture Notes in Business Information Processing, vol 476. Springer, Cham. https://doi.org/10.1007/978-3-031-33080-3_10

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  • DOI: https://doi.org/10.1007/978-3-031-33080-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33079-7

  • Online ISBN: 978-3-031-33080-3

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