| PENG Fei(彭斐),CHEN Shudong,QI Donglin,YU Yong,TONG Da.[J].高技术通讯(英文),2023,29(3):269~278 |
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| Semantic-aware graph convolution network on multi-hop paths for link prediction |
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| DOI：10. 3772/ j. issn. 1006-6748. 2023. 03. 005 |
| 中文关键词: |
| 英文关键词: knowledge graph ( KG), link prediction, graph convolution network ( GCN),knowledge graph completion (KGC),multi-hop paths,semantic information |
| 基金项目: |
| Author Name | Affiliation | | PENG Fei(彭斐) | (Institute of Microelectronics of the Chinese Academy of Sciences,Beijing 100029,P. R. China)
(University of Chinese Academy of Sciences,Beijing 101408,P. R. China) | | CHEN Shudong | | | QI Donglin | | | YU Yong | | | TONG Da | |
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| 中文摘要: |
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| 英文摘要: |
| Knowledge graph (KG) link prediction aims to address the problem of missing multiple valid triples in KGs. Existing approaches either struggle to efficiently model the message passing process of multi-hop paths or lack transparency of model prediction principles. In this paper,a new graph convolutional network path semantic-aware graph convolution network (PSGCN) is proposed to achieve modeling the semantic information of multi-hop paths. PSGCN first uses a random walk strategy to obtain all k-hop paths in KGs,then captures the semantics of the paths by Word2Sec and long shortterm memory (LSTM) models,and finally converts them into a potential representation for the graph convolution network (GCN) messaging process. PSGCN combines path-based inference methods and graph neural networks to achieve better interpretability and scalability. In addition,to ensure the robustness of the model,the value of the path threshold K is experimented on the FB15K-237 and WN18RR datasets,and the final results prove the effectiveness of the model. |
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