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.

我们提出了一个新颖的端到端框架，包含三个组成部分：多模态学习器、结构整合器和关系嵌入生成器，以整合多样化的多模态信息和知识图结构，从而促进零样本关系学习。通过对两个多模态知识图的评估结果，证明了我们提出方法的卓越性能。