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Inter-domain Multi-relational Link Prediction

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Machine Learning and Knowledge Discovery in Databases. Research Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12976))

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Abstract

Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities. Similar to other graph-structured data, link prediction is one of the most important tasks on multi-relational graphs and is often used for knowledge completion. When related graphs coexist, it is of great benefit to build a larger graph via integrating the smaller ones. The integration requires predicting hidden relational connections between entities belonged to different graphs (inter-domain link prediction). However, this poses a real challenge to existing methods that are exclusively designed for link prediction between entities of the same graph only (intra-domain link prediction). In this study, we propose a new approach to tackle the inter-domain link prediction problem by softly aligning the entity distributions between different domains with optimal transport and maximum mean discrepancy regularizers. Experiments on real-world datasets show that optimal transport regularizer is beneficial and considerably improves the performance of baseline methods.

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Notes

  1. 1.

    The code is available at https://github.com/phucdoitoan/inter-domain_lp.

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Acknowledgement

The authors would like to thank the anonymous reviewers for their insightful suggestions and constructive feedback.

Author information

Authors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Luu Huu Phuc, Koh Takeuchi & Hisashi Kashima

  2. Fujitsu Research, Fujitsu Ltd, Kanagawa, Japan

    Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi & Koji Maruhashi

Authors
  1. Luu Huu Phuc
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  2. Koh Takeuchi
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  3. Seiji Okajima
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  4. Arseny Tolmachev
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  5. Tomoyoshi Takebayashi
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  6. Koji Maruhashi
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  7. Hisashi Kashima
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Corresponding author

Correspondence to Luu Huu Phuc .

Editor information

Editors and Affiliations

  1. ELLIS - The European Laboratory for Learning and Intelligent Systems, Alicante, Spain

    Nuria Oliver

  2. ETHZ and EPFL, Zürich, Switzerland

    Fernando Pérez-Cruz

  3. Johannes Gutenberg University of Mainz, Mainz, Germany

    Stefan Kramer

  4. École Polytechnique, Palaiseau, France

    Jesse Read

  5. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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Phuc, L.H. et al. (2021). Inter-domain Multi-relational Link Prediction. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12976. Springer, Cham. https://doi.org/10.1007/978-3-030-86520-7_18

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  • DOI: https://doi.org/10.1007/978-3-030-86520-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86519-1

  • Online ISBN: 978-3-030-86520-7

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