Knowledge Commons of Institute of Automation,CAS
Spatial-temporal knowledge graph network for event prediction | |
Huai, Zepeng1,2; Zhang, Dawei2; Yang, Guohua2; Tao, Jianhua3 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
2023-10-07 | |
卷号 | 553页码:11 |
通讯作者 | Huai, Zepeng() |
摘要 | Predicting multiple concurrent events has a remarkable effect on understanding social dynamics and acting in advance to reduce damage. (1) From the perspective of spatial connection, trans-regional implication, which means the cause of the incident is not local but somewhere else, is an important reason for the occurrence of events. However, existing works neglect to model this spatial influence and only leverage the local information for event prediction. (2) From the perspective of temporal connection, future events are triggered by the continuous evolution of the region. Nonetheless, most studies assign events to different timestamps and recognize their sequential patterns, ignoring the continuity of the evolution process. To tackle the above two problems, we propose a spatial and temporal knowledge graph neural network (STKGN). Specifically, to construct the cross-regional connection, we propose a novel spatial-temporal event graph, where each region is denoted as a node and trans-regional influences are reflected by bidirectional edges. To simulate the continuously evolving process, we propose an event-driven memory network to represent the state of each entity and continually update the state embeddings by emerging events. Then we use a broadcast network to spread the local changes in the graph to obtain high-order reasons like the trans-regional implication. Extensive experiments on two realworld datasets demonstrate that STKGN achieves significant improvements over state-of-the-art methods. Further analysis shows the interpretability of the trans-regional implication. |
关键词 | Multi -event prediction Knowledge graph Dynamic graph embedding |
DOI | 10.1016/j.neucom.2023.126557 |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:001047469300001 |
出版者 | ELSEVIER |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54060 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Huai, Zepeng |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 3.Tsinghua Univ, Dept Automat, Beijing, Peoples R China |
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 | Huai, Zepeng,Zhang, Dawei,Yang, Guohua,et al. Spatial-temporal knowledge graph network for event prediction[J]. NEUROCOMPUTING,2023,553:11. |
APA | Huai, Zepeng,Zhang, Dawei,Yang, Guohua,&Tao, Jianhua.(2023).Spatial-temporal knowledge graph network for event prediction.NEUROCOMPUTING,553,11. |
MLA | Huai, Zepeng,et al."Spatial-temporal knowledge graph network for event prediction".NEUROCOMPUTING 553(2023):11. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论