Multiple Information Graph Convolutional Network Entity Alignment Method

NAVIGATE

Table of Contents

STATISTICS

Viewed21

Downloads38

Multiple Information Graph Convolutional Network Entity Alignment Method

[HTML] PDF下载 (38)

[1]XU Zhenshun,ZHANG Wenhao,WANG Zhenbiao,et al.Multiple Information Graph Convolutional Network Entity Alignment Method[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):108-116.[doi:10.13705/j.issn.1671-6833.2026.03.010]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 3 Page number: 108-116 Column: Public date: 2026-05-27

Title:: Multiple Information Graph Convolutional Network Entity Alignment Method

Author(s):: XU Zhenshun^1,2, ZHANG Wenhao^1,2, WANG Zhenbiao^1,2, TANG Zengjin^1,2, ZHAO Zeyu^1,2, SU Mengyao^1,2; 1.College of Compute Science and Engineering, North Minzu University, Yinchuan 750021, China; 2.The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China

Keywords:: knowledge graph; graph convolutional network; entity alignment; graph attention network; pre-trained languages model

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2026.03.010

Abstract:: Due to the heterogeneity in entity representation, relationship definition, and semantic structure between different knowledge graphs, it is difficult to effectively improve alignment quality in the presence of structural differences and information loss in the graphs by relying solely on graph structure. Therefore, in this study a graph convolutional entity alignment method that integrates multiple information was proposed. Firstly, the improved PageRank algorithm was used to filter triplets and alleviate the impact of differences in knowledge graph structure. Next, we learn the embedding representations of entities and attributes were learnt through graph convolutional networks, and the relationships between entities were iteratively updated by using these representations. Finally, based on the PBAB method, text description information was integrated and weighted with graph structure information to enhance the effectiveness of entity alignment. The experimental results showed that the proposed method improved the Hits@1 metric by approximately 3% compared to the optimal baseline, with corresponding improvements observed in other evaluation metrics as well.

References:: [1]李卫军, 顾建来, 张新勇, 等. 基于关系学习的小样本知识图谱补全模型[J]. 郑州大学学报(工学版), 2024, 45(4): 53-61.
LI W J, GU J L, ZHANG X Y, et al. Relation learning completion model for few-shot knowledge graphs[J]. Journal of Zhengzhou University (Engineering Science), 2024, 45(4): 53-61.
[2]FANOURAKIS N, EFTHYMIOU V, KOTZINOS D, et al. Knowledge graph embedding methods for entity alignment: experimental review[J]. Data Mining and Knowledge Discovery, 2023, 37(5): 2070-2137.
[3]庄严, 李国良, 冯建华. 知识库实体对齐技术综述[J]. 计算机研究与发展, 2016, 53(1): 165-192.
ZHUANG Y, LI G L, FENG J H. A survey on entity alignment of knowledge base[J]. Journal of Computer Research and Development, 2016, 53(1): 165-192.
[4]BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating embeddings for modeling multi-relational data[C]∥NIPS′13: Proceedings of the 26th International Conference on Neural Information Processing SystemsVolume 2, Advances in Neural Information Processing Systems. New York:ACM, 2013: 2787-2795.
[5]CHEN M H, TIAN Y T, YANG M H, et al. Multilingual knowledge graph embeddings for cross-lingual knowledge alignment[EB/OL]. (2017-05-017)[2025-03-04]. https:∥arxiv.org/abs/1611.03954v3.
[6]ZHU H, XIE R B, LIU Z Y, et al. Iterative entity alignment via joint knowledge embeddings[C]∥Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-2017).Melbourne: International Joint Conferences on Artificial Intelligence,2017:1-7.
[7]SUN Z Q, HU W, LI C K. Cross-lingual entity alignment via joint attribute-preserving embedding[M]∥The Semantic Web-ISWC 2017. Cham: Springer International Publishing, 2017: 628-644.
[8]SUN Z, HU W, ZHANG Q, et al. Bootstrapping entity alignment with knowledge graph embedding[C]∥27th International Joint Conference on Artificial Intelligence and 23rd European Conference on Artificial Intelligence: IJCAI-ECAI 2018. Melbourne: International Joint Conferences on Artificial Intelligence,2018:1-7.
[9]ZHANG Q H, SUN Z Q, HU W, et al. Multi-view knowledge graph embedding for entity alignment[EB/OL]. (2019-06-06)[2025-03-12]. https:∥arxiv.org/abs/1906.02390v1.
[10]WANG Z C, LV Q S, LAN X H, et al. Cross-lingual knowledge graph alignment via graph convolutional networks[C]∥Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels:ACL, 2018: 349-357.
[11] SUN Z Q, WANG C M, HU W, et al. Knowledge graph alignment network with gated multi-hop neighborhood aggregation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(1): 222-229.
[12]WU Y T, LIU X, FENG Y S, et al. Relation-aware entity alignment for heterogeneous knowledge graphs[EB/OL]. (2019-08-22)[2025-03-12]. https:∥arxiv.org/abs/1908.08210v1.
[13] ZHU Y, LIU H Z, WU Z H, et al. Relation-aware neighborhood matching model for entity alignment[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(5): 4749-4756.
[14]WU Y T, LIU X, FENG Y S, et al. Jointly learning entity and relation representations for entity alignment[EB/OL]. (2019-09-20)[2025-03-12]. https:∥arxiv.org/abs/1909.09317v1.
[15] BIANCHINI M, GORI M, SCARSELLI F. Inside PageRank[J]. ACM Transactions on Internet Technology, 2005, 5(1): 92-128.
[16] DEVLIN J, CHANG M W, LEE K, et al. BERT: pretraining of deep bidirectional transformers for language understanding[EB/OL]. (2019-05-24)[2025-0312]. https:∥arxiv.org/abs/1810.04805v2.
[17] JIANG T T, BU C Y, ZHU Y, et al. Combining embedding-based and symbol-based methods for entity alignment[J]. Pattern Recognition, 2022, 124: 108433.
[18] GAO J L, LIU X Y, CHEN Y B, et al. MHGCN: multiview highway graph convolutional network for cross-lingual entity alignment[J]. Tsinghua Science and Technology, 2022, 27(4): 719-728.
[19] SU Z H, XU T, DAI Y G, et al. Cross-lingual entity alignment method based on attribute weight updating network[J]. Journal of Northwestern Polytechnical University, 2024, 42(1): 157-164.
[20] LIANG Y, CAI W S, YANG M H, et al. An unsupervised multi-view contrastive learning framework with attention-based reranking strategy for entity alignment[J]. Neural Networks, 2024, 179: 106583.
[21] ZHU R B, MA M, WANG P. RAGA: relation-aware graph attention networks for global entity alignment[M]∥Advances in Knowledge Discovery and Data Mining. Cham: Springer International Publishing, 2021: 501513.

Similar References:

Memo

Last Update: 2026-05-27