用概率电路精简知识图谱补全规则集，大幅减少冗余规则。
Probabilistic Circuits for Knowledge Graph Completion with Reduced Rule Sets
- 基于概率电路学习规则协同分布，自动筛选高效规则组合。
- 规则数量减少70%-96%，性能仍达基线91%以上，最高提速31倍。
- 适用于追求可解释性与效率的工业级知识图谱系统。
基于规则的知识图谱补全方法虽具可解释性，但通常需数万条规则才能达到良好性能。尽管单次预测仅用少数规则，但全数据推理仍需庞大规则集，影响系统理解。本文通过概率电路学习规则协同的概率分布，实现规则集显著压缩。实验显示，规则数量减少70%-96%即可达到峰值基线性能；在等量最小规则集下，性能最高提升31倍；对比完整规则集，最小规则集仍保持91%的峰值性能。8个基准数据集验证表明，新规则集利用率更高，更少规则被浪费，每次预测所需规则更少。该框架基于尼尔森概率逻辑语义，无需独立性假设，支持精确概率推断及高效下界评估。
原文摘要 · Abstract (English)
Rule-based methods for knowledge graph completion provide explainable results, but often require tens of thousands of rules to achieve competitive performance. Although individual predictions may use only a few rules, reasoning over an entire dataset requires these massive rule sets, hampering system-level understanding. We address this by learning a probability distribution over sets of rules that work together using probabilistic circuits. Our approach achieves a 70-96% reduction in the number of rules needed to reach peak baseline performance. Using an equivalent minimal number of rules, we outperform the baseline by up to 31$\times$. When comparing our minimal rule sets against baseline's full rule sets, we preserve 91% of peak baseline performance. Empirical validation on 8 benchmark datasets shows that our reduced rule sets exhibit higher utilization---fewer rules are wasted, and each prediction requires fewer rules. We show that our framework is grounded in well-known semantics of Nilsson's probabilistic logic and does not require independence assumptions. We provide exact probabilistic inference as well as an efficient lower bound and evaluate both.
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