ME3A: A Multimodal Entity Entailment framework for multimodal Entity Alignment

Abstract

Current methods for multimodal entity alignment (MEA) primarily rely on entity representation learning, which undermines entity alignment performance because of cross-KG interaction deficiency and multimodal heterogeneity. In this paper, we propose a Multimodal Entity Entailment framework of multimodal Entity Alignment task, ME³A, and recast the MEA task as an entailment problem about entities in the two KGs. This way, the cross-KG modality information directly interacts with each other in the unified textual space. Specifically, we construct the multimodal information in the unified textual space as textual sequences: for relational and attribute modalities, we combine the neighbors and attribute values of entities as sentences; for visual modality, we map the entity image as trainable prefixes and insert them into sequences. Then, we input the concatenated sequences of two entities into the pre-trained language model (PLM) as an entailment reasoner to capture the unified fine-grained correlation pattern of the multimodal tokens between entities. Two types of entity aligners are proposed to model the bi-directional entailment probability as the entity similarity. Extensive experiments conducted on nine MEA datasets with various modality combination settings demonstrate that our ME${}^3$A effectively incorporates multimodal information and surpasses the performance of the state-of-the-art MEA methods by 16.5% at most.