本体驱动的概念模型的FAIR目录

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tiago Prince Sales , Pedro Paulo F. Barcelos , Claudenir M. Fonseca , Isadora Valle Souza , Elena Romanenko , César Henrique Bernabé , Luiz Olavo Bonino da Silva Santos , Mattia Fumagalli , Joshua Kritz , João Paulo A. Almeida , Giancarlo Guizzardi
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引用次数: 0

摘要

多领域模型目录是关于特定领域、关于建模语言结构的使用以及关于该语言模型中横切不同领域的模式和反模式的知识和见解的经验来源。如果按照公认的科学数据管理质量要求构建,它们可以支持领域和语言学习、模型重用、人类知识发现以及可靠的自动化处理和分析。更具体地说,与科学(元)数据不同,模型应该根据FAIR原则(可查找性、可访问性、互操作性和可重用性)进行共享。在本文中,我们报告了本体驱动概念建模研究的FAIR模型目录的构建,这是一种位于概念建模和本体工程交叉点的趋势范式,其中统一基础本体(UFO)和OntoUML是最常用的技术之一。目录,可在https://w3id.org/ontouml-models,目前包括一百四十多个模型,在各种上下文和领域中开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A FAIR catalog of ontology-driven conceptual models

Multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language’s constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. They may support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis if built following generally accepted quality requirements for scientific data management. More specifically, not unlike scientific (meta)data, models should be shared according to the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. The catalog, publicly available at https://w3id.org/ontouml-models, currently includes over one hundred and forty models, developed in a variety of contexts and domains.

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来源期刊
Data & Knowledge Engineering
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
66
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.
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