将优化后的规则推导还原为原始规则解释，提升知识图谱推理可解释性。
Recovering Explanations from Transformed Rule-Based Ontologies
- 从重写规则的证明反推原始规则推导路径。
- 证明还原问题为NP完全，但特定规则语言下可高效求解。
- 适用于需要追溯推理过程的知识系统开发与调试。
Datalog规则常用于定义知识图谱上的本体。规则推理器通常通过重写规则以提升评估效率，此类变换虽保留蕴含事实，但破坏了原始推导结构。在重写规则下生成的证明树能说明某事实为何成立，却难以还原为原始规则的解释。本文研究如何从重写规则下的蕴含证明中重构原始规则下的证明：确立了该问题的计算复杂度，并识别出两种实际中重要的规则语言用于指定证明变换。
原文摘要 · Abstract (English)
Datalog rules are often used to define ontologies over Knowledge Graphs. Rule reasoners routinely optimise such ontologies by rewriting their rules into a form that can be evaluated more efficiently. These transformations preserve the entailed facts, but not the structure of the underlying derivations. A proof tree under the rewritten rules explains why a fact holds, but does not readily yield an explanation in terms of the original rules. We study the problem of constructing, from a proof of entailment under the rewritten rules, a proof under the original ones: we establish its computational complexity and identify two practically relevant languages for specifying proof transformations.
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