Psychological work on memory, in particular by Bartlett (1932), has led the conclusion that people faced with a new situation use large amounts of highly structured knowledge acquired from previous experience. Bartlett used the word schema to refer to this phenomenon. Minsky (1975), his famous paper, proposed the notion of a frame as a fundamental structure used in natural language understanding, as well as in scene analysis. I will use the former term in the rest of this chapter, in spite of its general connotation. The main thesis defended by Bartlett was that the phenomena of memorization and remembering are both constructive and selective. The hypothesis has more recently been revived by psychologists working on discourse structure (Collins, 1978; Bransford and Franks, 1971; Kintsch, 1976). Various experiments performed on subjects who were told stories and then asked to describe what they remembered showed that people not only forget facts but add some. Moreover, they are unable to distinguish between what they have actually heard and what they have inferred. People hearing a story make assumptions, which they might revise or refine as more information comes in, either confirmatory or contradictory. Making such assumptions entails building (or retrieving) models of the expected text contents. A corollary of this process is that if the story adequately fits the model people have in mind, the story will be understood more easily. This chal)ter is based on a technical memo (HPP-79-25) from the Heuristic Programming lh( iect, l)cparmlent of Computer Science, Stanford University.

The use of Structured English as a computation independent knowledge representation format for non-technical users in business rules representation has been proposed in OMGs Semantics and Business Vocabulary Representation (SBVR). In the legal domain we face a similar problem. Formal representation languages, such as OASIS LegalRuleML and legal ontologies (LKIF, legal OWL2 ontologies etc.) support the technical knowledge engineer and the automated reasoning. But, they can be hardly used directly by the legal domain experts who do not have a computer science background. In this paper we adapt the SBVR Structured English approach for the legal domain and implement a proof-of-concept, called KR4IPLaw, which enables legal domain experts to represent their knowledge in Structured English in a computational independent and hence, for them, more usable way. The benefit of this approach is that the underlying pre-defined semantics of the Structured English approach makes transformations into formal languages such as OASIS LegalRuleML and OWL2 ontologies possible. We exemplify our approach in the domain of patent law.