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Learning higher-order features for relation prediction in knowledge hypergraph
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AI
DOI：10.1016/j.knosys.2024.111510.png)
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Abstract
En 中文
Knowledge Hypergraph (KHG) is a higher -order extension of the Knowledge Graph (KG), and its relation prediction is based on known data to predict unknown higher -order relations, thereby providing useful knowledge services. However, the existing KHG relation algorithms still have some limitations: (i) most studies only consider the influence of the direct neighbors, and (ii) they ignore the complex interactions existing inside higher -order facts. Based on this, we propose a KHG relation prediction model HoGCNF2 based on higher -order hypergraph convolutional network and feature fusion. Dual -channel hypergraph convolutional network considers the significant and higher -order information propagation of entities. Feature fusion strategy considers different types of higher -order structures. Besides, attention mechanism adaptively assigns weights to the learned embeddings. Extensive experiments demonstrate the superiority of HoGCNF2 on different datasets. Specifically, the MRR result improves by 2.6% on the unfixed dataset FB-AUTO, and improves by 9.7% on the fixed dataset WikiPeople-4. Our implementations are publicly available at: https://doi.org/10.24433/CO. 5584354.v1.
Keywords:
Knowledge hypergraph
Relation prediction
Hypergraph convolutional networks
Higher-order structure
Feature fusion
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K
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7.6
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1.3W
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4.5W
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INFORMATION FUSION
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INFORMATION FUSIONIF15.5
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