Aiming to accurately predict missing edges representing relations between entities, which are pervasive in real-world Knowledge Graphs (KGs), relation prediction plays a critical role in enhancing the comprehensiveness and utility of KGs. Recent research focuses on path-based methods due to their inductive and explainable properties. However, these methods face a great challenge when lots of reasoning paths do not form Closed Paths (CPs) in the KG. To address this challenge, we propose Anchoring Path Sentence Transformer (APST) by introducing Anchoring Paths (APs) to alleviate the reliance of CPs. Specifically, we develop a search-based description retrieval method to enrich entity descriptions and an assessment mechanism to evaluate the rationality of APs. APST takes both APs and CPs as the inputs of a unified Sentence Transformer architecture, enabling comprehensive predictions and high-quality explanations. We evaluate APST on three public datasets and achieve state-of-the-art (SOTA) performance in 30 of 36 transductive, inductive, and few-shot experimental settings.

翻译：旨在准确预测表示实体间关系的缺失边（这在现实世界知识图谱中普遍存在），关系预测在提升知识图谱的全面性与实用性方面发挥着关键作用。近期研究聚焦于基于路径的方法，因其具备归纳与可解释特性。然而，当大量推理路径在知识图谱中无法形成闭合路径时，此类方法面临重大挑战。为解决该问题，我们通过引入锚定路径来减轻对闭合路径的依赖，提出锚定路径句变换器模型。具体而言，我们开发了基于搜索的描述检索方法以丰富实体描述，并设计了评估机制来衡量锚定路径的合理性。锚定路径句变换器将锚定路径与闭合路径共同作为统一句变换器架构的输入，从而实现全面的预测与高质量的解释。我们在三个公开数据集上评估了锚定路径句变换器的性能，在36组直推式、归纳式及小样本实验设置中取得了30项最优结果。