Negation in Cognitive Reasoning
Negation in Cognitive Reasoning
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DOI：
10.1007/978-3-030-87626-5_16
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发表时间：
2020-12
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影响因子：
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通讯作者：
C. Schon;Sophie Siebert;Frieder Stolzenburg
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文献类型：
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作者：
C. Schon;Sophie Siebert;Frieder Stolzenburg
否定是形式逻辑和自然语言中的一种操作，通过这种操作，一个命题被一个陈述相反的命题所取代，例如通过添加“不”或另一个否定线索。以适当的方式对待否定是认知推理所必需的，认知推理旨在模拟人类在不完整和不一致的知识下得出有意义结论的能力。认知推理的任务之一是回答由自然语言中的句子给出的问题。有一些基于话语表示理论的工具可以将句子自动转换为形式逻辑表示，并且可以使用公式和知识数据库中的谓词名称添加额外的知识。然而，在实际应用中，逻辑数据库中的知识往往是不完整的。因此，自动推理系统的正向推理不足以得出问题的答案，因为，而不是完整的证明，往往只有部分肯定的知识可以得到，而否定的知识只在推理过程中使用。因此，我们的目标是消除句法否定，严格地说，被否定的事件或属性。在本文中，我们描述了一个有效的程序来确定否定的事件或属性，以取代它的逆。这奠定了认知推理的基础，采用逻辑和机器学习来回答一般问题。我们评估我们的程序的几个基准，并证明其在我们的认知推理系统的实际用途。
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.