Anchoring Path for Inductive Relation Prediction in Knowledge Graphs

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.

Authors

• Zhixiang Su School of Computer Science and Engineering, Nanyang Technological University (NTU), Singapore Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), NTU, Singapore WeBank-NTU Joint Research Institute on Fintech, NTU, Singapore
• Di Wang
• Chunyan Miao Nanyang Technological University
• Lizhen Cui Shandong University

Keywords

• DMKM: Linked Open Data, Knowledge Graphs & KB Completio
• DMKM: Rule Mining & Pattern Mining
• KRR: Common-Sense Reasoning
• KRR: Knowledge Acquisition