Attention-based Multi-hop Reasoning for Knowledge Graph
Wang Zikang1,2; Li Linjing1; Zeng Daniel1,2; Chen Yue3
2018-11
Conference NameIEEE International Conference on Intelligence and Security Informatics
Conference Date2018.11.08-10
Conference Place美国,迈阿密
Abstract

Knowledge graph plays an important role in de- tection, prediction, early warning, and other security related applications. A fundamental task in applying knowledge graph is the so-called multi-hop reasoning, which focuses on inferring new relations between entities. In this paper, we introduce attention mechanism to the classic compositional method. After finding reasoning paths between entities, we aggregate these paths’ embeddings into one according to their attentions, and infer the relation of entities based on the combined embedding. Two experiments on NELL-995 dataset, fact prediction and link prediction, validated that our method outperforms all baselines.

Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/44374
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.CNCERT/CC
3.chool of Computer and Control Engineering, University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang Zikang,Li Linjing,Zeng Daniel,et al. Attention-based Multi-hop Reasoning for Knowledge Graph[C],2018.
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