用低成本规则排序方法，快速学习可解释的逻辑模型。
Efficiently Learning Probabilistic Logical Models by Cheaply Ranking Mined Rules
- 提出规则效用度量，线性时间计算并排序挖掘出的逻辑规则。
- SPECTRUM框架在CPU上运行速度<1%主流神经网络，且准确率更高。
- 适合需要高可解释性的实际应用，如医疗、金融推理场景。
概率逻辑模型是神经符号AI的核心，对需要高可解释性的任务至关重要。与神经网络不同，其逻辑理论通常依赖领域专家手工构建，成本高且易出错。现有数据驱动学习算法普遍计算开销巨大，难以应用于真实场景。本文定义逻辑规则的精确率与召回率，并将其组合为规则效用，作为逻辑理论预测能力的低成本度量。提出SPECTRUM框架，通过线性时间挖掘数据图中的重复子图，并利用可线性计算的效用度量高效排序生成规则。理论证明所学逻辑理论具有保障的效用。实验表明，SPECTRUM在多个任务中可扩展至更大数据集，在CPU上的运行时间不足当前最优神经网络在GPU上耗时的1%，同时获得更高准确率。
原文摘要 · Abstract (English)
Probabilistic logical models are a core component of neurosymbolic AI and are important in their own right for tasks that require high explainability. Unlike neural networks, logical theories that underlie the model are often handcrafted using domain expertise, making their development costly and prone to errors. While there are algorithms that learn logical theories from data, they are generally prohibitively expensive, limiting their applicability in real-world settings. Here, we introduce precision and recall for logical rules and define their composition as rule utility - a cost-effective measure of the predictive power of logical theories. We also introduce SPECTRUM, a scalable framework for learning logical theories from relational data. Its scalability derives from a linear-time algorithm for mining recurrent subgraphs in the data graph along with a second algorithm that, using a utility measure that can be computed in linear time, efficiently ranks rules derived from these subgraphs. Finally, we prove theoretical guarantees on the utility of the learnt logical theory. As a result, we demonstrate across various tasks that SPECTRUM scales to larger datasets, often learning more accurate logical theories on CPUs in < 1% the runtime of SOTA neural network approaches on GPUs.
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。