Generic knowledge: acquisition and representation

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.