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摘要翻译：
我们定义了一个推理系统来捕获基于因果陈述的解释，使用IS-A层次结构形式的本体。我们首先引入一种简单的逻辑语言，它使表达一个事实引起另一个事实，一个事实解释另一个事实成为可能。我们提出了一组从因果陈述到解释陈述的形式化推理模式。我们引入了一个基本的本体，它在保持与命题推理接近的同时，给系统提供了更大的表达能力。我们提供了一个推理系统，它首先在纯命题框架中捕获所讨论的模式，然后在datalog（有限谓词）框架中捕获模式。
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英文标题：
《Ontology-based inference for causal explanation》
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作者：
Philippe Besnard (INRIA - IRISA, IRIT), Marie-Odile Cordier (INRIA -
IRISA), Yves Moinard (INRIA - IRISA)
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
We define an inference system to capture explanations based on causal statements, using an ontology in the form of an IS-A hierarchy. We first introduce a simple logical language which makes it possible to express that a fact causes another fact and that a fact explains another fact. We present a set of formal inference patterns from causal statements to explanation statements. We introduce an elementary ontology which gives greater expressiveness to the system while staying close to propositional reasoning. We provide an inference system that captures the patterns discussed, firstly in a purely propositional framework, then in a datalog (limited predicate) framework.
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PDF链接：
https://arxiv.org/pdf/1004.4801
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