20.500.12556/DKUM-92293 Knowledge graph alignment network with node-level strong fusion Entity alignment refers to the process of discovering entities representing the same object in different knowledge graphs (KG). Recently, some studies have learned other information about entities, but they are aspect-level simple information associations, and thus only rough entity representations can be obtained, and the advantage of multi-faceted information is lost. In this paper, a novel node-level information strong fusion framework (SFEA) is proposed, based on four aspects: structure, attribute, relation and names. The attribute information and name information are learned first, then structure information is learned based on these two aspects of information through graph convolutional network (GCN), the alignment signals from attribute and name are already carried at the beginning of the learning structure. In the process of continuous propagation of multi-hop neighborhoods, the effect of strong fusion of structure, attribute and name information is achieved and the more meticulous entity representations are obtained. Additionally, through the continuous interaction between sub-alignment tasks, the effect of entity alignment is enhanced. An iterative framework is designed to improve performance while reducing the impact on pre-aligned seed pairs. Furthermore, extensive experiments demonstrate that the model improves the accuracy of entity alignment and significantly outperforms 13 previous state-of-the-art methods. knowledge graph entity ealignment graph convolutional network knowledge fusion grafi poravanava entitet konvolucijski grafi true false true MDPI AG Angleški jezik Slovenski jezik https://www.mdpi.com/journal/applsci © 2022 by the authors Članek v reviji 2025-03-27 11:16:51 2025-03-27 11:16:51 2025-03-28 03:10:32 0000-00-00 00:00:00 2022 0 0 16 str. iss. 19 Vol. 12 20 Sep. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2022-08-04 2022-09-18 2022-09-20 004.8 126251779 10.3390/app12199434 2076-3417 applsci-12-09434-v2.pdf applsci-12-09434-v2.pdf 1 926A1DE61E8E1C4ECECB5DBB200132C2 6fbd8db2cf7b654f0291ec808e6528099e7aebffb19e9f9f4a5844cea1be4f03 efbb112c-0af4-11f0-8bc4-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=189728 https://www.mdpi.com/2076-3417/12/19/9434 0 f3662ff5-0af4-11f0-8bc4-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=189729 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0