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Relational Structure-Aware Knowledge Graph Representation in Complex Space

Author

Listed:
  • Ke Sun

    (School of Software, Dalian University of Technology, Dalian 116620, China)

  • Shuo Yu

    (School of Software, Dalian University of Technology, Dalian 116620, China)

  • Ciyuan Peng

    (Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3353, Australia)

  • Yueru Wang

    (Department of Mathematics, National Tsing Hua University, Hsinchu 30013, Taiwan)

  • Osama Alfarraj

    (Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia)

  • Amr Tolba

    (Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia)

  • Feng Xia

    (Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3353, Australia)

Abstract

Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification.

Suggested Citation

  • Ke Sun & Shuo Yu & Ciyuan Peng & Yueru Wang & Osama Alfarraj & Amr Tolba & Feng Xia, 2022. "Relational Structure-Aware Knowledge Graph Representation in Complex Space," Mathematics, MDPI, vol. 10(11), pages 1-16, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1930-:d:831550
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    References listed on IDEAS

    as
    1. Hui Fang & Chongcheng Chen & Yunfei Long & Ge Xu & Yongqiang Xiao, 2022. "DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph," Mathematics, MDPI, vol. 10(9), pages 1-16, April.
    2. Qi Lin & Shuo Yu & Ke Sun & Wenhong Zhao & Osama Alfarraj & Amr Tolba & Feng Xia, 2022. "Robust Graph Neural Networks via Ensemble Learning," Mathematics, MDPI, vol. 10(8), pages 1-14, April.
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    Cited by:

    1. Xuechen Zhao & Jinfeng Miao & Fuqiang Yang & Shengnan Pang, 2024. "Geometry Interaction Embeddings for Interpolation Temporal Knowledge Graph Completion," Mathematics, MDPI, vol. 12(13), pages 1-15, June.

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