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Two Birds with One Stone: A Link Prediction Model for Knowledge Hypergraph Based on Fully-Connected Tensor Decomposition

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 14177))

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Abstract

Knowledge hypergraph link prediction aims to predict missing relationships in knowledge hypergraphs and is one of the effective methods for graph completion. The existing optimal knowledge hypergraph link method based on tensor decomposition, i.e., GETD (Generalized Model based on Tucker Decomposition and Tensor Ring Decomposition), has achieved good performance by extending Tucker decomposition, but there are still two main problems: (1)GETD does not establish operations or connections between any two tensor factors, resulting in limited representation of tensor correlation (referred to as finiteness); (2)The tensor decomposed by GETD is highly sensitive to the arrangement of tensor patterns (referred to as sensitivity). In response to the above issues, we propose a knowledge hypergraph link prediction model, called GETD\(^+\), based on fully-connected tensor decomposition(FCTN). By combining Tucker decomposition and FCTN, a multi-linear operation/connection is established for any two factor tensors obtained from tensor decomposition. This not only enhances the representation ability of tensors, but also eliminates sensitivity to tensor pattern arrangement. Finally, the superiority of the GETD\(^+\) model was verified through a large number of experiments on real knowledge hypergraph datasets and knowledge graph datasets.

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Acknowledgment

The work is supported by the National Natural Science Foundation of China (No.62372342, No. 61702381).

Author information

Authors and Affiliations

  1. Wuhan University of Science and Technology, Hubei, 430065, China

    Jun Pang & Xiao-Qi Liu

  2. Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System, Hubei, 430065, China

    Jun Pang

  3. Beijing Institute of Technology, Beijing, 100081, China

    Hong-Chao Qin

  4. Wuhan Jiangang Middle School, Hubei, 430050, China

    Yan Liu

Authors
  1. Jun Pang

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  2. Hong-Chao Qin

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  3. Yan Liu

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  4. Xiao-Qi Liu

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Corresponding author

Correspondence toHong-Chao Qin.

Editor information

Editors and Affiliations

  1. Northeastern University, Shenyang, China

    Xiaochun Yang

  2. The University of Indonesia, Depok, Indonesia

    Heru Suhartanto

  3. Beijing Institute of Technology, Beijing, China

    Guoren Wang

  4. Northeastern University, Shenyang, China

    Bin Wang

  5. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  6. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Bing Li

  7. Sun Yat-sen University, Guangzhou, China

    Huaijie Zhu

  8. Anhui University, Hefei, China

    Ningning Cui

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Pang, J., Qin, HC., Liu, Y., Liu, XQ. (2023). Two Birds with One Stone: A Link Prediction Model for Knowledge Hypergraph Based on Fully-Connected Tensor Decomposition. In: Yang, X.,et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14177. Springer, Cham. https://doi.org/10.1007/978-3-031-46664-9_6

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