mlsanityData to 7 Oct 2026

Zero-Shot Relational Learning for Multimodal Knowledge Graphs

arXiv 2404.06220 ↗First posted 9 Apr 2024cs.LG cs.MMcited by 3

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Abstract (arXiv)

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 three multimodal knowledge graphs demonstrate the superior performance of our proposed method.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 37 of its 65 references that are in the map. First posted on arXiv 9 Apr 2024.

Builds on

37 of 65 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Diagnosing and Mitigating Modality Interference in Multimodal Large Language Modelscited by 18
  2. 2024Privately Learning from Graphs with Applications in Fine-tuning Large Language Modelscited by 1
  3. 2025DiffusionCom: Structure-Aware Multimodal Diffusion Model for Multimodal Knowledge Graph Completioncited by 1