| XU Hao (许 浩),CHEN Shudong,QI Donglin,TONG Da,YU Yong,CHEN Shuai.[J].高技术通讯(英文),2024,30(3):271~279 |
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| Hyperbolic hierarchical graph attention network for knowledge graph completion |
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| DOI：10. 3772 / j. issn. 1006-6748. 2024. 03. 006 |
| 中文关键词: |
| 英文关键词: hyperbolic space, link prediction, knowledge graph embedding, knowledge graph completion (KGC) |
| 基金项目: |
| Author Name | Affiliation | | XU Hao (许 浩) | (Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, P. R. China)
(University of Chinese Academy of Sciences, Beijing 100049, P. R. China) | | CHEN Shudong | | | QI Donglin | | | TONG Da | | | YU Yong | | | CHEN Shuai | |
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| 中文摘要: |
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| 英文摘要: |
| Utilizing graph neural networks for knowledge embedding to accomplish the task of knowledge
graph completion (KGC) has become an important research area in knowledge graph completion.
However, the number of nodes in the knowledge graph increases exponentially with the depth of the
tree, whereas the distances of nodes in Euclidean space are second-order polynomial distances,
whereby knowledge embedding using graph neural networks in Euclidean space will not represent the
distances between nodes well. This paper introduces a novel approach called hyperbolic hierarchical
graph attention network (H2GAT) to rectify this limitation. Firstly, the paper conducts knowledge
representation in the hyperbolic space, effectively mitigating the issue of exponential growth of nodes
with tree depth and consequent information loss. Secondly, it introduces a hierarchical graph atten-
tion mechanism specifically designed for the hyperbolic space, allowing for enhanced capture of the
network structure inherent in the knowledge graph. Finally, the efficacy of the proposed H2GAT
model is evaluated on benchmark datasets, namely WN18RR and FB15K-237, thereby validating its
effectiveness. The H2GAT model achieved 0. 445, 0. 515, and 0. 586 in the Hits@ 1, Hits@ 3 and
Hits@ 10 metrics respectively on the WN18RR dataset and 0. 243, 0. 367 and 0. 518 on the FB15K-
237 dataset. By incorporating hyperbolic space embedding and hierarchical graph attention, the
H2GAT model successfully addresses the limitations of existing hyperbolic knowledge embedding
models, exhibiting its competence in knowledge graph completion tasks. |
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