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Relation Extraction for Knowledge Base Completion: A Supervised Approach

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Semantic Web Challenges (SemWebEval 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 927))

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Abstract

This paper outlines our approach to the extraction on predefined relations from unstructured data (OKE Challenge 2018: Task 3). Our solution uses a deep learning classifier receiving as input raw sentences and a pair of entities. Over the output of the classifier, expert rules are applied to delete known erroneous relations. For training the system we gathered data by aligning DBPedia relations and Wikipedia pages. This process was mainly automatic, applying some filters to refine the training records by human supervision. The final results show that the combination of a powerful classifier model with expert knowledge have beneficial implications in the final performance of the system.

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Notes

  1. 1.

    Freebase data dumps https://developers.google.com/freebase/.

  2. 2.

    NLP Interchange Format http://persistence.uni-leipzig.org/nlp2rdf/.

  3. 3.

    Spacy https://spacy.io/.

  4. 4.

    Pytorch http://pytorch.org/.

  5. 5.

    This last capability was discarded due to its bad performance.

  6. 6.

    The “anchor” is the textual representation of an entity in a sentence.

  7. 7.

    https://project-hobbit.eu/.

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Authors and Affiliations

  1. Gradiant, Edificio CITEXVI, local 14, Universidade de Vigo, Vigo, Spain

    Héctor Cerezo-Costas & Manuela Martín-Vicente

Authors
  1. Héctor Cerezo-Costas
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  2. Manuela Martín-Vicente
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Correspondence to Héctor Cerezo-Costas .

Editor information

Editors and Affiliations

  1. Laboratoire d'Informatique de Paris-Nord, Paris 13 University, Villetaneuse, France

    Davide Buscaldi

  2. University of Bologna, Bologna, Italy

    Aldo Gangemi

  3. Department of Mathematics and Computer Science, University of Cagliari, Cagliari, Italy

    Diego Reforgiato Recupero

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Cerezo-Costas, H., Martín-Vicente, M. (2018). Relation Extraction for Knowledge Base Completion: A Supervised Approach. In: Buscaldi, D., Gangemi, A., Reforgiato Recupero, D. (eds) Semantic Web Challenges. SemWebEval 2018. Communications in Computer and Information Science, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-00072-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-00072-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00071-4

  • Online ISBN: 978-3-030-00072-1

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