Negation is both an operation in formal logic and in natural language by which a proposition is replaced by one stating the opposite, as by the addition of "not" or another negation cue. Treating negation in an adequate way is required for cognitive reasoning, which aims at modeling the human ability to draw meaningful conclusions despite incomplete and inconsistent knowledge. One task of cognitive reasoning is answering questions given by sentences in natural language. There are tools based on discourse representation theory to convert sentences automatically into a formal logic representation, and additional knowledge can be added using the predicate names in the formula and knowledge databases. However, the knowledge in logic databases in practice always is incomplete. Hence, forward reasoning of automated reasoning systems alone does not suffice to derive answers to questions because, instead of complete proofs, often only partial positive knowledge can be derived, while negative knowledge is used only during the reasoning process. In consequence, we aim at eliminating syntactic negation, strictly speaking, the negated event or property. In this paper, we describe an effective procedure to determine the negated event or property in order to replace it by its inverse. This lays the basis of cognitive reasoning, employing both logic and machine learning for general question answering. We evaluate our procedure by several benchmarks and demonstrate its practical usefulness in our cognitive reasoning system.
翻译:认知推理需要以适当的方式处理否定问题,其目的在于模拟人的能力,以得出有意义的结论,尽管知识不完整和不一致。认知推理的一项任务就是回答自然语言判决提出的问题。根据讨论代表理论,可以将判决自动转换成正式逻辑代表,并且可以使用公式和知识数据库中的前提名称来增加额外知识。然而,逻辑数据库中的知识始终是不完整的。因此,单靠自动推理系统的远端推理不足以找到问题答案,因为没有完整的证据,往往只能产生部分的积极知识,而消极知识只是在推理过程中才使用。因此,我们的目标是消除理论否定,严格地说,否定的事件或财产。在本文中,我们描述一种有效的程序,用以确定否定的事件或财产,以取代它。我们用逻辑推理法和机器推理法来解释我们的一般问题。我们用一些实际的推理推理推理来证明我们的系统是有用的。我们用逻辑推理和机器推理来回答一般问题。