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LGHAE: Local and Global Hyper-relation Aggregation Embedding for Link Prediction

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Data Science (ICPCSEE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1880))

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Abstract

The Knowledge Graph (KGs) have profoundly impacted many research fields. However, there is a problem of low data integrity in KGs. The binary-relational knowledge graph is more common in KGs but is limited by less information. It often has less content to use when predicting missing entities (relations). The hyper-relational knowledge graph is another form of KGs, which introduces much additional information (qualifiers) based on the main triple. The hyper-relational knowledge graph can effectively improve the accuracy of predicting missing entities (relations). The existing hyper-relational link prediction methods only consider the overall perspective when dealing with qualifiers and calculate the score function by combining the qualifiers with the main triple. However, these methods overlook the inherent characteristics of entities and relations. This paper proposes a novel Local and Global Hyper-relation Aggregation Embedding for Link Prediction (LGHAE). LGHAE can capture the semantic features of hyper-relational data from local and global perspectives. To fully utilize local and global features, Hyper-InteractE, as a new decoder, is designed to predict missing entities to fully utilize local and global features. We validated the feasibility of LGHAE by comparing it with state-of-the-art models on public datasets.

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Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, China

    Peikai Yuan

  2. Institute of Systems Engineering, Academy of Military Sciences, Beijing, 100089, China

    Zhenheng Qi, Hui Sun & Chao Liu

Authors
  1. Peikai Yuan
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  2. Zhenheng Qi
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  3. Hui Sun
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  4. Chao Liu
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Corresponding author

Correspondence to Zhenheng Qi .

Editor information

Editors and Affiliations

  1. Harbin Engineering University, Harbin, China

    Zhiwen Yu

  2. Harbin Engineering University, Harbin, China

    Qilong Han

  3. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Hongzhi Wang

  4. Northwestern Polytechnical University, Xi'an, China

    Bin Guo

  5. Shiga University, Shiga, Japan

    Xiaokang Zhou

  6. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  7. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Yuan, P., Qi, Z., Sun, H., Liu, C. (2023). LGHAE: Local and Global Hyper-relation Aggregation Embedding for Link Prediction. In: Yu, Z., et al. Data Science. ICPCSEE 2023. Communications in Computer and Information Science, vol 1880. Springer, Singapore. https://doi.org/10.1007/978-981-99-5971-6_26

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  • DOI: https://doi.org/10.1007/978-981-99-5971-6_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5970-9

  • Online ISBN: 978-981-99-5971-6

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