Anchoring Path for Inductive Relation Prediction in Knowledge Graphs
Zhixiang Su Di Wang Chunyan Miao Lizhen Cui
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.