Research on Entity Alignment Models for Multimodal Knowledge Graphs
TL;DR
A novel multimodal entity alignment model- TextFusionEA is proposed, which implements structural embedding based on GraphSAGE, uses MobileNetV3 to obtain visual features, introduces an adaptive fusion mechanism of semantic, structural, and visual features, and designs a semi supervised iterative learning strategy to extend the seed subset to alleviate the problem of data scarcity.
Abstract
The entity alignment task in Multi modal Knowledge Graph (MKG) faces challenges such as strong modal heterogeneity, large semantic gap, and inflexible fusion strategies. Existing methods generally rely on static weighted fusion and large amounts of annotated data, making it difficult to cope with complex multimodal environments. This article proposes a novel multimodal entity alignment model- TextFusionEA, which implements structural embedding based on GraphSAGE, uses MobileNetV3 to obtain visual features, introduces an adaptive fusion mechanism of semantic, structural, and visual features, and designs a semi supervised iterative learning strategy to extend the seed subset to alleviate the problem of data scarcity. Experiments on five datasets have shown that TextFusionEA performs well in MRR Hits@1. It significantly outperforms existing mainstream methods in terms of metrics, verifying its alignment performance and robustness in complex multimodal scenarios.