Knowledge Acquisition最新文献

Two studies were designed to evaluate the efficiency of decision table representations for constructing and comprehending expert system rules by nonprogrammers with no experience in either knowledge engineering or expert systems. The first study compared the speed and accuracy of a decision table editor for constructing rules in a tabular representation relative to a standard text editor. Rules were constructed faster and more accurately with the decision table editor than with the text editor. The second study focused on the representational value of decision tables for comprehending expert system rules. In a verification task, subjects responded to questions of different types as accurately and rapidly as possible on the basis of the logical structure of a set of rules represented in either a decision table or textual format. The decision table showed an advantage only in situations where the diagrammatic, integral representation of the decision table expedited the perceptual and symbolic matching processes involved in the search.

We propose a method for constructing domain knowledge and explain it by using the office domain as an example. Our method produces generic concepts which cover the domain within their scope. They serve as tools for modelling applications, and enable model builders to adopt comprehensive and unbiased points of view. These concepts possess such practical properties because they implement ontological principles, i.e. the most important ways of viewing and discriminating a domain's objects. When built in a principled manner, domain concepts are highly modular; they can be refined and assembled without overlap to form case models. Ontological principles can further guide model builders in decomposing a modelling task.

Metatool support for knowledge acquisition is an approach to automate the implementation of domain-specific knowledge-acquisition tools. Dots is a metatool that can be used by developers to generate domain-specific knowledge-acquisition tools. Whenever a domain model useful for expressing the relevant expertise can be established, developers can use Dots to specify and generate a knowledge-acquisition environment for development of expert systems. Dots assumes that the knowledge-acquisition tools generated are based on the knowledge-elicitation technique of graphical knowledge editing. A salient aspect of Dots is that no particular domain, task or problem-solving method is presupposed by the metatool. We achieve this generalization by introducing an abstract-architecture view—that is, an architectural model of the target knowledge-acquisition tool—as the framework for specifying target knowledge-acquisition tools. Dots provides facilities for editing this abstract architecture and for instantiating knowledge-acquisition tools from it.

This paper illustrates a technique for discovering mutual implications among hierarchically structured data. Such a technique may be applied to both knowledge and data bases. If the hierarchical structure makes it possible to define granularity levels, mutual implications can be evaluated at any level. Results can be quantitative (i.e. a degree in the range [0, 1]) or qualitative (i.e. a label taken from a user-defined set). If the ground data do not represent a mapping among individuals, i.e. the level of information granularity is not the highest, a local approximation based on T-Norms can be used. The process of implication discovery allows one to derive inference rules for expert systems and to detect default values. In addition, it might be successfully used by sophisticated machine learning algorithms.