Anchoring Path for Inductive Relation Prediction in Knowledge Graphs
This addresses the problem of incomplete knowledge graphs for AI applications, offering an incremental improvement over existing path-based methods.
The paper tackles the challenge of predicting missing relations in knowledge graphs when many reasoning paths are not closed, proposing Anchoring Path Sentence Transformer (APST) which uses both anchoring and closed paths, achieving state-of-the-art performance in 30 out of 36 experimental settings.
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.