Elaborating analogies from conceptual models

Abstract

This article defines and analyzes a computational model of similarity which detects analogies between objects based on conceptual descriptions of them, constructed from classification, generalization relations, and attributes. Analogies are detected (elaborated by functions which measure conceptual distances between objects with respect to these semantic modeling abstractions. The model is domain independent and operational upon objects described in nonuniform ways. It does not require any special forms of knowledge for identifying analogies and distinguishes the importance of distinct object elements. Also, it has a polynomial complexity. Due to these characteristics, it may be used in complex tasks involving intra- or interdomain analogical reasoning. So far the similarity model has been applied in the domain of software engineering. First, to support the specification of software requirements by analogical reuse and second, to enable the integration of requirements specifications, generated by the multiple agents involved in information system development. Details of these applications can be found in cited references. Also, we have conducted an empirical evaluation of: (i) the consistency of the estimates generated by the model against human intuition about similarity and (ii) its recall performance in tasks of analogical retrieval, the results of which are presented in this article.