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Knowledge graph completion with low-dimensional gated hierarchical hyperbolic embedding
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DOI：10.1016/j.knosys.2024.112804.png)
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摘要
En 中文
Knowledge graph embedding aims to advance learning performance by deliberating the complexity of hierarchical data structures in a finite-dimensional space. Most traditional Euclidean models struggle to accurately describe and navigate intricate hierarchical relationships between nodes. Hyperbolic embedding methods, particularly when combined with graph neural networks, offer promise ineffectively modeling complex hierarchies in a limited-dimensional space. These methods can incorporate more information about nodes under the same conditions but are also more susceptible to interference from complex neighborhood features. This paper introduces HyperGatE, which novelty combines a hyperbolic graph hierarchical neighbor structure based on a gated filter and a hyperbolic isometric rotation model for knowledge graph completion. By analyzing the hierarchical structure of the knowledge base and using a gated mechanism to form multi-layer embedding networks of entity-relational paths, the gated selects diverse uneven multi-hop relations within the entity neighborhood and measures them through graph attention networks. Furthermore, the simplified hyperbolic isometric model allows for uncovering hierarchical structures in the data. HyperGatE experimentally surpasses some state-of-the-art Euclidean and hyperbolic space models in high and low dimensions across four well-known data, namely WN18RR, FB15k-237, NELL-995, and Kinship.
Keyword:
Knowledge graph
Link prediction
Graph attention networks
Poincar & eacute
ball model
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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