Zero-shot link prediction (ZSLP) on knowledge graphs aims at automatically identifying relations between given entities. Existing methods primarily employ auxiliary information to predict tail entity given head entity and its relation, yet face challenges due to the occasional unavailability of such detailed information and the inherent simplicity of predicting tail entities based on semantic similarities. Even though Large Language Models (LLMs) offer a promising solution to predict unobserved relations between the head and tail entity in a zero-shot manner, their performance is still restricted due to the inability to leverage all the (exponentially many) paths' information between two entities, which are critical in collectively indicating their relation types. To address this, in this work, we introduce a Condensed Transition Graph Framework for Zero-Shot Link Prediction (CTLP), which encodes all the paths' information in linear time complexity to predict unseen relations between entities, attaining both efficiency and information preservation. Specifically, we design a condensed transition graph encoder with theoretical guarantees on its coverage, expressiveness, and efficiency. It is learned by a transition graph contrastive learning strategy. Subsequently, we design a soft instruction tuning to learn and map the all-path embedding to the input of LLMs. Experimental results show that our proposed CTLP method achieves state-of-the-art performance on three standard ZSLP datasets

翻译：零样本链接预测（ZSLP）旨在自动识别知识图谱中给定实体间的关系。现有方法主要利用辅助信息，根据头实体及其关系预测尾实体，然而由于此类详细信息有时不可用，且基于语义相似性预测尾实体本身较为简单，这些方法面临挑战。尽管大型语言模型（LLMs）提供了一种有前景的零样本方式预测头实体与尾实体间未观测关系，但其性能仍受限于无法充分利用两实体间所有（指数级数量）路径的信息，而这些路径对于共同指示关系类型至关重要。为解决此问题，本文提出一种用于零样本链接预测的紧凑转移图框架（CTLP），该框架以线性时间复杂度编码所有路径信息，预测实体间未见关系，同时实现高效性与信息保持。具体而言，我们设计了一个具有理论保证的紧凑转移图编码器，涵盖其覆盖率、表达能力与效率，并通过转移图对比学习策略进行训练。随后，我们设计一种软指令微调方法，学习并映射全路径嵌入至LLMs的输入。实验结果表明，所提出的CTLP方法在三个标准ZSLP数据集上达到了最先进的性能。