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Recent years have witnessed the successful application of knowledge graph techniques in structured data processing, while how to incorporate knowledge from visual and textual modalities into knowledge graphs has been given less attention. To better organize them, Multimodal Knowledge Graphs (MKGs), comprising the structural triplets of traditional Knowledge Graphs (KGs) together with entity-related multimodal data (e.g., images and texts), have been introduced consecutively. However, it is still a great challenge to explore MKGs due to their inherent incompleteness. Although most existing Multimodal Knowledge Graph Completion (MKGC) approaches can infer missing triplets based on available factual triplets and multimodal information, they almost ignore the modal conflicts and supervisory effect, failing to achieve a more comprehensive understanding of entities. To address these issues, we propose a novel H ierarchical K nowledge A lignment ( HKA ) framework for MKGC. Specifically, a macro-knowledge alignment module is proposed to capture global semantic relevance between modalities for dealing with modal conflicts in MKG. Furthermore, a micro-knowledge alignment module is also developed to reveal the local consistency information through inter- and intra-modality supervisory effects more effectively. By integrating different modal predictions, a final decision can be made. Experimental results on three benchmark MKGC tasks have demonstrated the effectiveness of the proposed HKA framework.
Xu et al. (Sat,) studied this question.