Fusion-Optimized Multimodal Entity Alignment with Textual Descriptions

Multimodal knowledge graph entity alignment is a key basic task of knowledge fusion and integration, which is used to identify entities with semantic equivalent but different representation forms in different knowledge graphs. Previous entity alignment research has mostly focused on encoding and utilizing basic features such as entity names and attributes; however, it is difficult to comprehensively capture the rich semantic information of entities by solely relying on these basic features. To effectively overcome this limitation, this paper proposes a fusion-optimized multimodal entity alignment method, FMEA-TD. Compared with previous work, this method makes full use of the textual description information in the knowledge graph to provide rich supplements for entity features, thereby better capturing the entity semantics and solving the problems faced by relying solely on the entity’s own features. FMEA-TD is able to effectively fuse the entity’s own information and text description information through multimodal cooperation confidence, establish the interaction mechanism between them, and thus promote mutual collaboration between different modalities, which enhances the model’s ability to understand the semantic text. Experimentally validated, FMEA-TD outperforms current state-of-the-art baseline methods on public knowledge graph datasets.