  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 comprises commonsense reasoning and
text comprehension. 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 logical
representation. However, since the knowledge in logical databases in practice
always is incomplete, 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. In consequence,
negative information from negated expressions does not help in this context,
because only negative knowledge can be derived from this. Therefore, we aim at
reducing syntactic negation, strictly speaking, the negated event or property,
to its inverse. This lays the basis of cognitive reasoning employing both logic
and machine learning for general question answering. In this paper, we describe
an effective procedure to determine the negated event or property in order to
replace it with it inverse and our overall system for cognitive reasoning. We
demonstrate the procedure with examples and evaluate it with several
benchmarks.
