用大模型让知识图谱零样本学习新实体关系，无需训练即可推理。
A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding
- 用大模型的文本提示获取未见实体语义信息，实现跨模态迁移。
- 在多个真实数据集上优于现有方法，显著提升未见类别建模能力。
- 适合开放域场景下需快速适配新类别、无标注数据的研究者。
零样本学习（ZL）对处理未见类别任务至关重要，如自然语言处理、图像分类和跨语言迁移。当前方法常无法准确推断涉及未见类别的新关系或实体，严重限制其在开放域场景下的可扩展性和实用性。零样本学习面临如何有效在多模态知识图谱（MMKG）嵌入表示学习中传递未见类别的语义信息的挑战。本文提出ZSLLM框架，利用大语言模型（LLMs）对未见类别的文本模态信息作为提示，充分调动大模型的推理能力，实现未见类别在不同模态间的语义信息迁移。通过基于模型的学习方式，增强未见类别在MMKG中的嵌入表示。在多个真实世界数据集上的大量实验表明，该方法优于当前最优方法。
原文摘要 · Abstract (English)
Zero-shot learning (ZL) is crucial for tasks involving unseen categories, such as natural language processing, image classification, and cross-lingual transfer.Current applications often fail to accurately infer and handle new relations orentities involving unseen categories, severely limiting their scalability and prac-ticality in open-domain scenarios. ZL learning faces the challenge of effectivelytransferring semantic information of unseen categories in multi-modal knowledgegraph (MMKG) embedding representation learning. In this paper, we proposeZSLLM, a framework for zero-shot embedding learning of MMKGs using largelanguage models (LLMs). We leverage textual modality information of unseencategories as prompts to fully utilize the reasoning capabilities of LLMs, enablingsemantic information transfer across different modalities for unseen categories.Through model-based learning, the embedding representation of unseen cate-gories in MMKG is enhanced. Extensive experiments conducted on multiplereal-world datasets demonstrate the superiority of our approach compared tostate-of-the-art methods.
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