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Dynamic relation learning for link prediction in knowledge hypergraphs

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Abstract

Link prediction for knowledge graphs (KGs), which aims to predict missing facts, has been broadly studied in binary relational KGs. However, real world data contains a large number of high-order interaction patterns, which is difficult to describe using only binary relations. In this work, we propose a relation-based dynamic learning model RD-MPNN, based on the message passing neural network model, to learn higher-order interactions and address the link prediction problem in knowledge hypergraphs. Different from existing methods, we consider the positional information of entities within a hyper-relation to differentiate each entity’s role in the hyper-relation. Furthermore, we complete the representation learning of hyper-relations by dynamically updating hyper-relations with entity information. Extensive evaluations on two representative knowledge hypergraph datasets demonstrate that our model outperforms the state-of-the-art methods. We also compare the performance of models at differing arities (the number of entities within a relation), to show that RD-MPNN demonstrates outstanding performance metrics for complex hypergraphs (arity>2).

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Availability of data and materials

The datasets generated during and/or analysed during the current study are available in the https://github.com/ooCher/RD-MPNN repository.

Code Availability

The code of the study is available in the https://github.com/ooCher/RD-MPNN repository

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Authors and Affiliations

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, No.4,Section 2,North Jianshe Road, Chengdu, 610054, Sichuan, China

    Xue Zhou, Bei Hui & Hao Wu

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Avenue, West Hi-tech Zone, Chengdu, 611731, Sichuan, China

    Ilana Zeira & Ling Tian

  3. CETC Rongwei Electronic Technology Co.,Ltd, Jinke North Road, Jinniu District, Chengdu, 610074, Sichuan, China

    Hao Wu

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  1. Xue Zhou
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Correspondence to Ling Tian.

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All authors declare that: (i) no support, financial or otherwise, has been received from any organization that may have an interest in the submitted work ; and (ii) there are no other relationships or activities that could appear to have influenced the submitted work.

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Bei Hui, Ilana Zeira, Hao Wu, and Ling Tian contributed equally to this work.

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Zhou, X., Hui, B., Zeira, I. et al. Dynamic relation learning for link prediction in knowledge hypergraphs. Appl Intell 53, 26580–26591 (2023). https://doi.org/10.1007/s10489-023-04710-5

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