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A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

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The Semantic Web – ISWC 2023 (ISWC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14265))

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Abstract

Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with specific semantics exhibiting graph patterns are an important factor in the performance of KGE models. Though KGE models’ capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern. To address this challenge, we evaluate the performance of 7 KGE models over 4 common relational patterns on 2 benchmarks, then conduct an analysis in theory, entity frequency, and part-to-whole three aspects and get some counterintuitive conclusions. Finally, we introduce a training-free method Score-based Patterns Adaptation (SPA) to enhance KGE models’ performance over various relational patterns. This approach is simple yet effective and can be applied to KGE models without additional training. Our experimental results demonstrate that our method generally enhances performance over specific relational patterns. Our source code is available from GitHub at https://github.com/zjukg/Comprehensive-Study-over-Relational-Patterns.

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Acknowledgements

This work is funded by Zhejiang Provincial Natural Science Foundation of China (No. LQ23F020017), Yongjiang Talent Introduction Programme (2022A-238-G), the National Natural Science Foundation of China (NSFCU19B2027, NSFC91846204), joint project DH-2022ZY0012 from Donghai Lab.

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Authors and Affiliations

  1. School of Software Technology, Zhejiang University, Hangzhou, China

    Long Jin, Zhen Yao & Wen Zhang

  2. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Mingyang Chen & Huajun Chen

  3. Donghai laboratory, Hangzhou, China

    Huajun Chen

Authors
  1. Long Jin
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  2. Zhen Yao
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  3. Mingyang Chen
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  4. Huajun Chen
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  5. Wen Zhang
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Corresponding author

Correspondence to Wen Zhang .

Editor information

Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Terry R. Payne

  2. University of Bologna, Bologna, Italy

    Valentina Presutti

  3. Southeast University, Nanjing, China

    Guilin Qi

  4. Universidad Politécnica de Madrid, Madrid, Spain

    María Poveda-Villalón

  5. Huawei Technologies R&D UK, Edinburgh, UK

    Giorgos Stoilos

  6. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Laura Hollink

  7. IT University of Copenhagen, Copenhagen, Denmark

    Zoi Kaoudi

  8. Nanjing University, Nanjing, China

    Gong Cheng

  9. Tsinghua University, Beijing, Beijing, China

    Juanzi Li

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Jin, L., Yao, Z., Chen, M., Chen, H., Zhang, W. (2023). A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning. In: Payne, T.R., et al. The Semantic Web – ISWC 2023. ISWC 2023. Lecture Notes in Computer Science, vol 14265. Springer, Cham. https://doi.org/10.1007/978-3-031-47240-4_16

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  • DOI: https://doi.org/10.1007/978-3-031-47240-4_16

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  • Online ISBN: 978-3-031-47240-4

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