A Formalization of Knowledge‐Level Models for Knowledge Acquisition

This article defines second‐generation knowledge acquisition as a modeling activity that is knowledge‐level oriented. Knowledge‐level models of expert reasoning represent an important output of the knowledge‐acquisition process, since they describe, in a conceptual and implementation‐independent fashion, the different roles and types of knowledge required for a problem‐solving task. We argue that a formalization of such models enhances knowledge acquisition, and in particular the conceptualization phase, by rendering currently informal concepts and intuitions more precise, thus also contributing to a more solid basis for KBS design, validation, and maintenance. A framework is constructed for the formal specification of knowledge‐level models. The proposed formalism, called ml2, has been inspired by the kads methodology for KBS development, and aims at expressing different roles and types of knowledge components through employing an order‐sorted logic, a modular structuring of theories, and a meta‐level organization of knowledge, comprising “enlarged” reflection rules and a “meaningful” naming relation. An application of the formal specification method to heuristic classification is given. Issues relating to the epistemological adequacy and the computational tractability of formalized knowledge‐level models are discussed.