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Sowohl bei der Entwicklung konventioneller Software als auch bei der Entwicklung wissensbasierter Systeme fehlen z.Z. systematische Ansätze, Anforderungen an das zu entwickelnde Produkt „ingenieurmäßig“ zu erheben. Die Probleme, mit denen sich der Software Engineer konfrontiert sieht, ähneln denen der Wissensakquisition im Knowledge Engineering. Der an der Universität Karlsruhe am Institut AIFB entwickelte Ansatz MIKE ([AFL93]) beschreibt eine systematische Vorgehensweise zur Entwicklung wissensbasierter Systeme. Die Beschreibung der spezifischen Anforderungen an wissensbasierte Systeme ist Gegenstand der aktuellen Forschung; mit MIKE steht aber bereits das Gerüst zur Verfügung, mit denen die Anforderungen im Laufe der weiteren Entwicklungsphasen verwaltet werden können.
New KARL (Knowledge Acquisition and Representation Language) allows to specify all parts of a problem-solving method (PSM). It is a formal language with a well-defined semantics and thus allows to represent PSMs precisely and unambiguously yet abstracting from implementation detail. In this paper it is shown how the language KARL has been modified and extended to New KARL to better meet the needs for the representation of PSMs. Based on a conceptual structure of PSMs new language primitives are introduced for KARL to specify such a conceptual structure and to support the configuration of methods. An important goal for this extension was to preserve three important properties of KARL: to be (i) a conceptual, (ii) a formal, and (iii) an executable language.
A topic in the field of knowledge acquisition is the reuse of components that are described at the knowledge level. Problems concern the description, indexing and retrieval of components. In our case there is the additional feature of integrating so called automated building blocks in a knowledge level description. This paper describes what knowledge level descriptions of components for reuse should look like, and proposes a way to describe assumptions and requirements that are to be made explicit. In the paper an extension of the “normal” knowledge acquisition setting is made in the direction of machine learning components.
In the last years a common notion of a Problem-Solving Method (PSM) emerged from different knowledge engineering frameworks. As a generic description of the dynamic behaviour of knowledge based systems PSMs are favored subjects of reuse. Up to now, most investigations on the reuse of PSMs focus on static features and methods as objects of reuse. By this, they ignore a lot of information of how the PSM was developed that is, in principle, entailed in the different parts of a conceptual model of a PSM.
In this paper the information of the different parts of PSMs is reconsidered from a reuse process point of view. A framework for generalized problem-solving methods is presented that describes the structure of a category of methods based on family resemblances. These generalized methods can be used to structure libraries of PSMs and - in the process of reuse - as a means to derive an incarnation, i.e. a member of its family of PSMs.
For illustrating the ideas, the approach is applied to the task rsp. problem type of parametric design.
A library of software components should be essentially more than just a juxtaposition of its items. For problem-solving methods the notion of a family is suggested as means to cluster the items and to provide partially a structure of the library. This paper especially investigates how the similar control flows of the members of such a family can be described in one framework.
The central issue in corpus-driven linguistics is the detection and description of patterns in language usage. The features that constitute the notion of a pattern can be computed to a certain extent by statistical (collocation) methods, but a crucial part of the notion may vary depending on applications and users. Thus, typically, any computed collocation cluster will have to be interpreted hermeneutically. Often it might be captured by a generalized, more abstract pattern. We present a generic process model that supports the recognition, interpretation, and expression of the patterns inside and of the relations between clusters. By this, clusters can be merged virtually according to any notion of a 'pattern', and their relations can be exploited for different applications
This introductory tutorial describes a strictly corpus-driven approach for uncovering indications for aspects of use of lexical items. These aspects include ‘(lexical) meaning’ in a very broad sense and involve different dimensions, they are established in and emerge from respective discourses. Using data-driven mathematical-statistical methods with minimal (linguistic) premises, a word’s usage spectrum is summarized as a collocation profile. Self-organizing methods are applied to visualize the complex similarity structure spanned by these profiles. These visualizations point to the typical aspects of a word’s use, and to the common and distinctive aspects of any two words.