Knowledge Commons of Institute of Automation,CAS
Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs | |
Yang, Linyao1,2; Lv, Chen3; Wang, Xiao1,4; Qiao, Ji3; Ding, Weiping5; Zhang, Jun6; Wang, Fei-Yue1,4 | |
发表期刊 | IEEE/CAA Journal of Automatica Sinica |
ISSN | 2329-9266 |
2021 | |
卷号 | 9期号:11页码:1-15 |
通讯作者 | Wang, Xiao(x.wang@ia.ac.cn) |
摘要 | Knowledge graphs (KGs) have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services. In recent years, researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids. With multiple power grid dispatching knowledge graphs (PDKGs) constructed by different agencies, the knowledge fusion of different PDKGs is useful for providing more accurate decision supports. To achieve this, entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step. Existing entity alignment methods cannot integrate useful structural, attribute, and relational information while calculating entities’ similarities and are prone to making many-to-one alignments, thus can hardly achieve the best performance. To address these issues, this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments. This model proposes a novel knowledge graph attention network (KGAT) to learn the embeddings of entities and relations explicitly and calculates entities’ similarities by adaptively incorporating the structural, attribute, and relational similarities. Then, we formulate the counterpart assignment task as an integer programming (IP) problem to obtain one-to-one alignments. We not only conduct experiments on a pair of PDKGs but also evaluate our model on three commonly used cross-lingual KGs. Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs. |
关键词 | entity alignment integer programming knowledge fusion knowledge graph embedding power dispatch |
学科门类 | 工学 ; 工学::计算机科学与技术(可授工学、理学学位) |
DOI | 10.1109/JAS.2022.000000 |
关键词[WOS] | ENERGY |
URL | 查看原文 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key R&D Program of China[2018AAA0101502] ; Science and Technology Project of SGCC (State Grid Corporation of China) |
项目资助者 | National Key R&D Program of China ; Science and Technology Project of SGCC (State Grid Corporation of China) |
WOS研究方向 | Automation & Control Systems |
WOS类目 | Automation & Control Systems |
WOS记录号 | WOS:000866520600011 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48711 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Wang, Xiao |
作者单位 | 1.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.China Electric Power Research Institute 4.Qingdao Academy of Intelligent Industries 5.School of Information Science and Technology, Nantong University 6.School of Electrical Engineering and Automation, Wuhan University |
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 | Yang, Linyao,Lv, Chen,Wang, Xiao,et al. Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs[J]. IEEE/CAA Journal of Automatica Sinica,2021,9(11):1-15. |
APA | Yang, Linyao.,Lv, Chen.,Wang, Xiao.,Qiao, Ji.,Ding, Weiping.,...&Wang, Fei-Yue.(2021).Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs.IEEE/CAA Journal of Automatica Sinica,9(11),1-15. |
MLA | Yang, Linyao,et al."Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs".IEEE/CAA Journal of Automatica Sinica 9.11(2021):1-15. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Collective+Entity+Al(1600KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 | |
JAS-2021-1069.pdf(2494KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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