Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning
文献类型:会议论文
作者 | Mengqi Zhang1,2; Xuwei Xia3,4; Qiang Liu1,2; Shu Wu1,2; Liang Wang1,2 |
出版日期 | 2023-04-30 |
会议日期 | 2023-4-30 |
会议地点 | Austin, TX, USA |
英文摘要 | Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on historical TKG data. Most of the existing methods are incapable of explicitly modeling the long-term time dependencies from history and neglect the adaptive integration of the long- and short-term information. To tackle these problems, we propose a novel method that utilizes a designed Hierarchical Relational Graph Neural Network to learn the Long- and Short-term representations for TKG reasoning, namely HGLS. Specifically, to explicitly associate entities in different timestamps, we first transform the TKG into a global graph. Based on the built graph, we design a Hierarchical Relational Graph Neural Network that executes in two levels: The sub-graph level is to capture the semantic dependencies within concurrent facts of each KG. And the global-graph level aims to model the temporal dependencies between entities. Furthermore, we design a module to extract the long- and short-term information from the output of these two levels. Finally, the long- and short-term representations are fused into a unified one by Gating Integration for entity prediction. Extensive experiments on four datasets demonstrate the effectiveness of HGLS. |
源URL | [http://ir.ia.ac.cn/handle/173211/52299] |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | Qiang Liu |
作者单位 | 1.Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation, Chinese Academy of Sciences 2.School of Artifcial Intelligence, University of Chinese Academy of Sciences 3.School of Cyber Security, University of Chinese Academy of Sciences 4.Institute of Information Engineering, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Mengqi Zhang,Xuwei Xia,Qiang Liu,et al. Learning Long- and Short-term Representations for Temporal Knowledge Graph Reasoning[C]. 见:. Austin, TX, USA. 2023-4-30. |
入库方式: OAI收割
来源:自动化研究所
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