Papers › MMEA: Entity Alignment for Multi-Modal Knowledge Graphs
MMEA: Entity Alignment for Multi-Modal Knowledge Graphs
Liyi Chen, Zhi Li, Yijun Wang, Tong Xu, Zhefeng Wang, Enhong Chen
Entity alignment plays an essential role in the knowledge graph (KG) integration. Though large efforts have been made on exploring the association of relational embeddings between different knowledge graphs, they may fail to effectively describe and integrate the multimodal knowledge in the real application scenario. To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view. Specifically, we first design a novel multi-modal knowledge embedding method to generate the entity representations of relational, visual and numerical knowledge, respectively. Along this line, multiple representations of different types of knowledge will be integrated via a multimodal knowledge fusion module. Extensive experiments on two public datasets clearly demonstrate the effectiveness of the MMEA model with a significant margin compared with the state-of-the-art methods.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections