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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 7

Incorporating entity neighborhood information for temporal knowledge graph entity alignment

Zhai Shepinga,b
Zhang Wenjinga
Ma Mengyaoa
Yang Ruia
a. School of Computer Science & Technology, b. Shaanxi Key Laboratory of Network Data Analysis & Intelligent Processing, Xi'an University of Posts & Telecommunications, Xi'an 710121, China
DOI: 10.19734/j.issn.1001-3695.2024.12.0513

Abstract

The purpose of Entity Alignment is to build a comprehensive Knowledge Graph by matching identical entities across multi-source Knowledge Graphs. Existing methods primarily focus on static Knowledge Graphs, failing to fully utilize the abundant temporal information present in large-scale knowledge structures, and neglecting potential neighbors that are not directly connected while integrating entity neighborhood information. To address these challenges, an Entity Alignment model, ENTEA, was proposed to integrate neighborhood information into Temporal Knowledge Graphs. First, the temporal features of entities were constructed by capturing their activities at different time points. Next, a neighborhood similarity measurement method was introduced to identify potential neighboring nodes, enabling message passing across separate channels to extract diverse neighborhood representations and capture domain-specific features. Finally, the temporal and neighborhood features of entities were combined to generate enriched entity embeddings. Experimental results demonstrate that the proposed model improves Hits@1 by 1.8, 1.7, 1.7, and 1.9 percentage points on four real-world datasets, DICEWS-1K/200 and YAGO-WIKI50K-5K/1K, compared to the best-performing baseline models. These findings validate the effectiveness of the proposed approach.

Foundation Support

国家自然科学基金资助项目(61373116)
工业和信息化部通信软科学(2018-R-26)
陕西省重点研发计划资助项目(2022GY-038)
国家级大学生创新创业计划训练项目(202411664067)
陕西省大学生创新创业计划训练项目(S202411664140)
陕西省教育厅科学研究计划资金助项目(18JK0697)
陕西省社会科学基金资助项目(2016N008)
西安市社会科学规划基金资助项目(17X63)
西安邮电大学研究生创新基金资助项目(CXJJYL2023038)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.12.0513
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-14] Accepted Paper

Cite This Article

翟社平, 张文静, 马梦瑶, 等. 融合实体邻域信息的时序知识图谱实体对齐 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0513. (Zhai Sheping, Zhang Wenjing, Ma Mengyao, et al. Incorporating entity neighborhood information for temporal knowledge graph entity alignment [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0513. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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