On the Use of Case Based Reasoning Techniques in Flexible Querying

G. Tré, Tom Matthé, Parisa Kordjamshidi, Marysa Demoor
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引用次数: 9

Abstract

Case based reasoning (CBR) is a methodology where new problems are solved by investigating, adapting and reusing solutions to a previously solved, similar problem. Hereby knowledge is deduced from the characteristics of a collection of past cases, rather than induced from a set of knowledge rules that are stored in a knowledge base. In this paper we describe how fuzzy CBR techniques can be used to enhance the accessibility of relational databases, more specifically, flexible querying of regular relational databases. Two approaches are discussed: an approach where a database system is extended with a standalone instance- based prediction facility and an approach where such a prediction facility is embedded as an extension of the relational algebra. In both approaches, fuzzy set theory is used for the gradual modelling of similarity. Furthermore, its related possibility theory is used for the modelling of query satisfaction and for the handling of the inevitable uncertainty that occurs when predictions are made.
基于案例的推理技术在灵活查询中的应用
基于案例的推理(CBR)是一种通过调查、调整和重用先前解决的类似问题的解决方案来解决新问题的方法。因此,知识是从过去案例集合的特征中推导出来的,而不是从存储在知识库中的一组知识规则中推导出来的。本文描述了模糊CBR技术如何用于增强关系数据库的可访问性,更具体地说,是对常规关系数据库的灵活查询。本文讨论了两种方法:一种方法是用独立的基于实例的预测工具扩展数据库系统,另一种方法是将这种预测工具作为关系代数的扩展嵌入其中。在这两种方法中,模糊集理论被用于相似度的逐步建模。此外,其相关的可能性理论用于查询满意度的建模和处理预测时发生的不可避免的不确定性。
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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