  Relational learning is an essential task in the domain of knowledge
representation, particularly in knowledge graph completion (KGC).While
relational learning in traditional single-modal settings has been extensively
studied, exploring it within a multimodal KGC context presents distinct
challenges and opportunities. One of the major challenges is inference on newly
discovered relations without any associated training data. This zero-shot
relational learning scenario poses unique requirements for multimodal KGC,
i.e., utilizing multimodality to facilitate relational learning. However,
existing works fail to support the leverage of multimodal information and leave
the problem unexplored. In this paper, we propose a novel end-to-end framework,
consisting of three components, i.e., multimodal learner, structure
consolidator, and relation embedding generator, to integrate diverse multimodal
information and knowledge graph structures to facilitate the zero-shot
relational learning. Evaluation results on two multimodal knowledge graphs
demonstrate the superior performance of our proposed method.
