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Dynamic relation learning for link prediction in knowledge hypergraphs
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DOI：10.1007/s10489-023-04710-5.png)
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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).
Keyword:
Link prediction
Knowledge hypergraph
Message passing neural network
Dynamic relation learning
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3.5
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7.6K
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CORE: A knowledge graph entity type prediction method via complex space regression and emb e dding
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