Computer Studies Bldg. Room 209
AI is beginning to make some dents in the "knowledge acquisition
bottleneck", the problem of acquiring large amounts of general
world knowledge to support language understanding and commonsense
reasoning. Two text-based approaches to the problem are
(1) to abstract such knowledge from patterns of predication
and modification in miscellaneous texts, and (2) to derive such
knowledge by direct interpretation of general statements in
ordinary language, such as are found in lexicons and resources
like Open Mind. I will discuss the status of our efforts in
these directions (currently centered around the KNEXT system),
and the problems that are encountered. Among these problems are
what exactly is meant by generalities such as "Cats land on their
feet", and how this meaning should be formalized. One particular
difficulty is that such statements typically involve ``donkey
anaphora". I will suggest a ``dynamic Skolemization" approach
that leads naturally to script- or frame-like representations,
of the sort that have been developed in AI independently of
linguistic considerations.
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