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Ontology Completion Using Graph Convolutional Networks

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The Semantic Web – ISWC 2019 (ISWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11778))

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Abstract

Many methods have been proposed to automatically extend knowledge bases, but the vast majority of these methods focus on finding plausible missing facts, and knowledge graph triples in particular. In this paper, we instead focus on automatically extending ontologies that are encoded as a set of existential rules. In particular, our aim is to find rules that are plausible, but which cannot be deduced from the given ontology. To this end, we propose a graph-based representation of rule bases. Nodes of the considered graphs correspond to predicates, and they are annotated with vectors encoding our prior knowledge about the meaning of these predicates. The vectors may be obtained from external resources such as word embeddings or they could be estimated from the rule base itself. Edges connect predicates that co-occur in the same rule and their annotations reflect the types of rules in which the predicates co-occur. We then use a neural network model based on Graph Convolutional Networks (GCNs) to refine the initial vector representation of the predicates, to obtain a representation which is predictive of which rules are plausible. We present experimental results that demonstrate the strong performance of this method.

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Notes

  1. 1.

    http://wiki.opensemanticframework.org/index.php/Ontology_Best_Practices.

  2. 2.

    Implementation and data are available at https://github.com/bzdt/GCN-based-Ontology-Completion.git.

  3. 3.

    http://www.adampease.org/OP/.

  4. 4.

    https://www.w3.org/TR/2003/PR-owl-guide-20031215/wine.

  5. 5.

    http://reliant.teknowledge.com/DAML/Economy.owl.

  6. 6.

    http://reliant.teknowledge.com/DAML/Transportation.owl.

  7. 7.

    http://swat.cse.lehigh.edu/resources/onto/olympics.owl.

  8. 8.

    https://code.google.com/archive/p/word2vec/.

  9. 9.

    https://github.com/stardog-union/pellet.

  10. 10.

    https://docs.dgl.ai.

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Acknowledgements

Steven Schockaert was supported by ERC Starting Grant 637277. Zied Bouraoui was supported by CNRS PEPS INS2I MODERN.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Na Li

  2. CRIL - CNRS & University of Artois, Lens, France

    Zied Bouraoui

  3. Cardiff University, Cardiff, UK

    Steven Schockaert

Authors
  1. Na Li
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  2. Zied Bouraoui
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  3. Steven Schockaert
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Corresponding authors

Correspondence to Na Li , Zied Bouraoui or Steven Schockaert .

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Editors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  2. Linköping University, Linköping, Sweden

    Olaf Hartig

  3. University of Bonn, Bonn, Germany

    Maria Maleshkova

  4. University of Economics Prague, Prague, Czech Republic

    Vojtěch Svátek

  5. University of Illinois at Chicago, Chicago, IL, USA

    Isabel Cruz

  6. University of Chile, Santiago, Chile

    Aidan Hogan

  7. Memect Technology, Beijing, China

    Jie Song

  8. Mines Saint-Etienne, Saint-Etienne, France

    Maxime Lefrançois

  9. Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France

    Fabien Gandon

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Li, N., Bouraoui, Z., Schockaert, S. (2019). Ontology Completion Using Graph Convolutional Networks. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_25

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  • DOI: https://doi.org/10.1007/978-3-030-30793-6_25

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