Combining object-oriented representations of knowledge with proximity to conceptual prototypes

CompEuro 1992 Proceedings Computer Systems and Software Engineering Pub Date : 1992-05-04 DOI:10.1109/CMPEUR.1992.218443

K. Lano

引用次数: 8

Abstract

A framework for knowledge representation that combines the fuzzy reasoning of systems and object-oriented databases is suggested. The use of objects to represent knowledge has become popular. However, this organization of knowledge, as a classification of entities by means of their attributes and their characteristic operations, returns to a traditional view of the formation of concepts (H. Gardner, 1985). This view, that conceptual categories can all be defined in the crisp way that mathematical concepts are defined, is not plausible for many real-world examples, and the idea of categories as formed from a clustering of data around a conceptual prototype, with an associated nearness measure, was substituted in its place (E. Rosch, 1978). A system that combines these two apparently distinct means of representation is described. Machine learning techniques are applied to the formation of suitable metrics for concepts.<>

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将知识的面向对象表示与接近概念原型相结合

提出了一种将系统模糊推理与面向对象数据库相结合的知识表示框架。使用对象来表示知识已经变得很流行。然而，这种知识组织，作为实体的属性和特征操作的分类，回到了概念形成的传统观点(H. Gardner, 1985)。这种观点认为，概念范畴都可以用数学概念定义的清晰方式来定义，但对于许多现实世界的例子来说，这种观点是不可信的，而范畴的概念是由围绕概念原型的数据聚类形成的，并带有相关的接近度度量，这一观点被取代了(E. Rosch, 1978)。本文描述了一种结合了这两种明显不同的表示方式的系统。机器学习技术被应用于概念合适度量的形成。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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CompEuro 1992 Proceedings Computer Systems and Software Engineering

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