Temporal Knowledge Graph Reasoning Based on Entity Relationship Similarity Perception

被引:1
|
作者
Feng, Siling [1 ]
Zhou, Cong [1 ]
Liu, Qian [1 ]
Ji, Xunyang [1 ]
Huang, Mengxing [1 ]
机构
[1] Hainan Univ, Sch Informat & Commun Engn, Haikou 570228, Peoples R China
基金
中国国家自然科学基金;
关键词
temporal knowledge graph; similarity measurement method; similarity features;
D O I
10.3390/electronics13122417
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Temporal knowledge graphs (TKGs) are used for dynamically modeling facts in the temporal dimension, and are widely used in various fields. However, existing reasoning models often fail to consider the similarity features between entity relationships and static attributes, making it difficult for them to effectively handle these temporal attributes. Therefore, these models have limitations in dealing with previously invisible entities that appear over time and the implicit associations of static attributes between entities. To address this issue, we propose a temporal knowledge graph reasoning model based on Entity Relationship Similarity Perception, known as ERSP. This model employs the similarity measurement method to capture the similarity features of entity relationships and static attributes, and then fuses these features to generate structural representations. Finally, we provide a decoder with entity relationship representation, static attribute representation, and structural representation information to form a quadruple. Experiments conducted on five common benchmark datasets show that ERSP surpasses the majority of TKG reasoning methods.
引用
收藏
页数:20
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