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Refining Node Embeddings via Semantic Proximity

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The Semantic Web – ISWC 2020 (ISWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12506))

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Abstract

There is a variety of available approaches to learn graph node embeddings. One of their common underlying task is the generation of (biased) random walks that are then fed into representation learning techniques. Some techniques generate biased random walks by using structural information. Other approaches, also rely on some form of semantic information. While the former are purely structural, thus not fully considering knowledge available in semantically rich networks, the latter require complex inputs (e.g., metapaths) or only leverage node types that may not be available. The goal of this paper is to overcome these limitations by introducing NESP (Node Embeddings via Semantic Proximity), which features two main components. The first provides four different ways of biasing random walks by leveraging semantic relatedness between predicates. The second component focuses on refining (existing) embeddings by leveraging the notion of semantic proximity. This component iteratively refines an initial set of node embeddings imposing the embeddings of semantic neighboring nodes of a node to lie within a sphere of fixed radius. We discuss an extensive experimental evaluation and comparison with related work.

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Authors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands

    Melisachew Wudage Chekol

  2. Department of Computer Science, Sapienza University of Rome, Via Salaria 113, 00198, Rome, Italy

    Giuseppe Pirrò

Authors
  1. Melisachew Wudage Chekol
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  2. Giuseppe Pirrò
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Corresponding author

Correspondence to Giuseppe Pirrò .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Jeff Z. Pan

  2. University of Liverpool, Liverpool, UK

    Valentina Tamma

  3. University of Bari, Bari, Italy

    Claudia d’Amato

  4. University of California, Santa Barbara, Santa Barbara, CA, USA

    Krzysztof Janowicz

  5. California State University, Long Beach, Long Beach, CA, USA

    Bo Fu

  6. Vienna University of Economics and Business, Vienna, Austria

    Axel Polleres

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Oshani Seneviratne

  8. Massachusetts Institute of Technology, Cambridge, MA, USA

    Lalana Kagal

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Chekol, M.W., Pirrò, G. (2020). Refining Node Embeddings via Semantic Proximity. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12506. Springer, Cham. https://doi.org/10.1007/978-3-030-62419-4_5

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  • DOI: https://doi.org/10.1007/978-3-030-62419-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62418-7

  • Online ISBN: 978-3-030-62419-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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