评估本体驱动概念模型抽象的质量

IF 2.7 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Data & Knowledge Engineering Pub Date : 2024-07-14 DOI:10.1016/j.datak.2024.102342

Elena Romanenko , Diego Calvanese , Giancarlo Guizzardi

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引用次数: 0

摘要

本体驱动）概念模型的复杂性与设计该模型的领域和软件的复杂性密切相关。有鉴于此，我们之前提出了一种生成本体驱动概念模型抽象的算法。在本文中，我们对该算法生成的抽象的质量进行了实证评估。首先，我们在用本体驱动的概念模型语言 OntoUML 表示的模型 FAIR 目录上实现并测试了该算法的最后一个版本。其次，我们进行了三项用户研究，以评估建模者所感知的抽象结果的有用性。本文报告了这些实验的结果，并对如何利用这些结果改进现有算法进行了思考。

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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Evaluating quality of ontology-driven conceptual models abstractions

The complexity of an (ontology-driven) conceptual model highly correlates with the complexity of the domain and software for which it is designed. With that in mind, an algorithm for producing ontology-driven conceptual model abstractions was previously proposed. In this paper, we empirically evaluate the quality of the abstractions produced by it. First, we have implemented and tested the last version of the algorithm over a FAIR catalog of models represented in the ontology-driven conceptual modeling language OntoUML. Second, we performed three user studies to evaluate the usefulness of the resulting abstractions as perceived by modelers. This paper reports on the findings of these experiments and reflects on how they can be exploited to improve the existing algorithm.

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来源期刊

Data & Knowledge Engineering 工程技术-计算机：人工智能

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

6 months

期刊介绍： Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.