  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.
