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Adaptive-Skip-TransE Model: Breaking Relation Ambiguities for Knowledge Graph Embedding

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Knowledge Science, Engineering and Management (KSEM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11775))

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Abstract

Knowledge graph embedding aims to encode entities and relations into a low-dimensional vector space, obtaining its distributed vector representation for further knowledge learning and reasoning. Most existing methods assume that each relation owns one unique vector. However, in the real world, many relations are multi-semantic. We note that a reasonable adaptive learning method for the number of semantics for a given relation is lacking in knowledge graph embedding. In this paper, we propose a probabilistic model Skip-TransE, which comprehensively considers the two-way prediction ability and global loss intensity of the golden triplets. Then based on Skip-TransE, its non-parametric Bayesian extended model Adaptive-Skip-TransE is presented to automatically learn the number of semantics for each relation. Extensive experiments show that the proposed models can achieve some substantial improvements above the state-of-the-art baselines.

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Authors and Affiliations

  1. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Shoukang Han, Xiaobo Guo & Nan Mu

  2. State Key Laboratory of Information Security, Chinese Academy of Sciences, Beijing, China

    Shoukang Han, Xiaobo Guo, Lei Wang & Nan Mu

  3. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Shoukang Han, Xiaobo Guo, Lei Wang, Zeyi Liu & Nan Mu

Authors
  1. Shoukang Han
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  2. Xiaobo Guo
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  3. Lei Wang
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  4. Zeyi Liu
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  5. Nan Mu
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Corresponding author

Correspondence to Xiaobo Guo .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Christos Douligeris

  2. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  3. University of Piraeus, Piraeus, Greece

    Dimitris Apostolou

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Han, S., Guo, X., Wang, L., Liu, Z., Mu, N. (2019). Adaptive-Skip-TransE Model: Breaking Relation Ambiguities for Knowledge Graph Embedding. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_49

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  • DOI: https://doi.org/10.1007/978-3-030-29551-6_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29550-9

  • Online ISBN: 978-3-030-29551-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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