MPRM: A Markov Path-based Rule Miner for Efficient and Interpretable Knowledge Graph Reasoning
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916742379339776 |
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| author | Li, Mingyang Wang, Song Cai, Ning |
| author_facet | Li, Mingyang Wang, Song Cai, Ning |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_12329 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MPRM: A Markov Path-based Rule Miner for Efficient and Interpretable Knowledge Graph Reasoning Li, Mingyang Wang, Song Cai, Ning Artificial Intelligence Social and Information Networks 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. |
| title | MPRM: A Markov Path-based Rule Miner for Efficient and Interpretable Knowledge Graph Reasoning |
| topic | Artificial Intelligence Social and Information Networks |
| url | https://arxiv.org/abs/2505.12329 |