用马尔可夫路径挖掘知识图谱规则，高效又可解释。
MPRM: A Markov Path-based Rule Miner for Efficient and Interpretable Knowledge Graph Reasoning
- 将规则推理建模为马尔可夫链，用路径概率聚合计算置信度。
- 在百万级事实图上仅采样不到1%数据，22秒完成挖掘。
- 适合需要高效可解释推理的场景，如医疗、金融知识系统。
知识图谱中的规则挖掘能实现可解释的链接预测。然而，基于深度学习的规则挖掘方法在大规模知识图谱上面临显著的内存与时间挑战；传统方法虽采用采样技术降低开销，却受限于僵化的置信度度量，仍需高昂计算成本。为此，本文提出MPRM，一种新型规则挖掘方法：将基于规则的推理建模为马尔可夫链，并利用路径概率聚合推导出高效置信度度量，大幅降低计算负担。在多个数据集上的实验表明，MPRM可在单个CPU上以不足1%的采样比例，在22秒内完成百万级事实知识图谱的挖掘，同时保持可解释性，并使推理准确率较基线最高提升11%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。