Anchoring Path for Inductive Relation Prediction in Knowledge Graphs
DOI:
https://doi.org/10.1609/aaai.v38i8.28750Keywords:
DMKM: Linked Open Data, Knowledge Graphs & KB Completio, KRR: Knowledge Acquisition, KRR: Common-Sense Reasoning, DMKM: Rule Mining & Pattern MiningAbstract
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.Downloads
Published
2024-03-24
How to Cite
Su, Z., Wang, D., Miao, C., & Cui, L. (2024). Anchoring Path for Inductive Relation Prediction in Knowledge Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 38(8), 9011-9018. https://doi.org/10.1609/aaai.v38i8.28750
Issue
Section
AAAI Technical Track on Data Mining & Knowledge Management