MPRM: A Markov Path-based Rule Miner for Efficient and Interpretable Knowledge Graph Reasoning

TL;DR

MPRM introduces a Markov chain-based rule mining approach for knowledge graphs that significantly reduces computational costs while maintaining interpretability and improving inference accuracy.

Contribution

It presents a novel Markov path-based rule mining method with an efficient confidence metric, enabling scalable and accurate knowledge graph reasoning.

Findings

MPRM can mine large knowledge graphs with over a million facts in 22 seconds.

It samples less than 1% of facts on a CPU, demonstrating high efficiency.

MPRM boosts inference accuracy by up to 11% compared to baseline methods.

Abstract

Rule mining in knowledge graphs enables interpretable link prediction. However, deep learning-based rule mining methods face significant memory and time challenges for large-scale knowledge graphs, whereas traditional approaches, limited by rigid confidence metrics, incur high computational costs despite sampling techniques. To address these challenges, we propose MPRM, a novel rule mining method that models rule-based inference as a Markov chain and uses an efficient confidence metric derived from aggregated path probabilities, significantly lowering computational demands. Experiments on multiple datasets show that MPRM efficiently mines knowledge graphs with over a million facts, sampling less than 1% of facts on a single CPU in 22 seconds, while preserving interpretability and boosting inference accuracy by up to 11% over baselines.