Learning Latent Relations for Temporal Knowledge Graph Reasoning | |
Mengqi Zhang1,2![]() ![]() ![]() | |
2023 | |
会议名称 | Annual Meeting of the Association for Computational Linguistics |
会议日期 | 2023-7-9 |
会议地点 | Toronto, Canada |
摘要 | Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on historical data. However, due to the limitations in construction tools and data sources, many important associations between entities may be omitted in TKG. We refer to these missing associations as latent relations. Most of the existing methods have some drawbacks in explicitly capturing intra-time latent relations between co-occurring entities and inter-time latent relations between entities that appear at different times. To tackle these problems, we propose a novel Latent relations Learning method for TKG reasoning, namely L2TKG. Specifically, we first utilize a Structural Encoder (SE) to obtain representations of entities at each timestamp. We then design a Latent Relations Learning (LRL) module to mine and exploit the intra- and inter-time latent relations. Finally, we extract the temporal representations from the output of SE and LRL for entity prediction. Extensive experiments on four datasets demonstrate the effectiveness of L2TKG. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 知识表示与推理 |
国重实验室规划方向分类 | 社会信息感知与理解 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52300 |
专题 | 模式识别实验室 |
通讯作者 | Shu Wu |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation, Chinese Academy of Sciences 3.Institute of Information Engineering, Chinese Academy of Sciences 4.School of Cyber Security, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Mengqi Zhang,Yuwei Xia,Qiang Liu,et al. Learning Latent Relations for Temporal Knowledge Graph Reasoning[C],2023. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
论文4-Learning Latent (1574KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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