Multimodal Contextual Interactions of Entities: A Modality Circular Fusion Approach for Link Prediction

Link prediction aims to infer missing valid triplets to complete knowledge graphs, with recent inclusion of multimodal information to enrich entity representations. Existing methods project multimodal information into a unified embedding space or learn modality-specific features separately for later integration. However, performance was limited in such studies due to neglecting the modalities compatibility and conflict semantic carried by entities in valid and invalid triplets. In this paper, we aim at modeling inter-entity modality interactions and thus propose a novel Modality Circular fusion approach (MoCi), which interweaves multimodal contextual of entities. Firstly, unlike most methods in this task that directly fuse modalities, we design a triplets-prompt modality contrastive pre-training to align modality semantics beforehand. Moreover, we propose a modality circular fusion model using a simple yet efficient multilinear transformation strategy. This allows explicit inter-entity modality interactions, distinguishing it from methods confined to fuse within individual entities. To the best of our knowledge, MoCi presents one of the pioneering frameworks that tailored to grasp inter-entity modality semantics for better link prediction. Extensive experiments on seven datasets demonstrate our model yields SOTA performance, confirming the efficacy of MoCi in modeling inter-entity modality interactions. Our code is released at https://github.com/MoCiGitHub/MoCi.