Unraveling the foundations and the evolution of conceptual modeling—Intellectual structure, current themes, and trajectories

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jacky Akoka , Isabelle Comyn-Wattiau , Nicolas Prat , Veda C. Storey
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引用次数: 0

Abstract

The field of conceptual modeling has now been in existence for over five decades. To understand how this field has evolved and should continue to evolve, it is useful to examine the contributions made over time and the themes that have emerged. In this research, we apply bibliometric analysis to a corpus of over 4700 research papers spanning from 1976 to 2023. We successively apply co-citation, bibliographic coupling, and main path analysis. Co-citation and citation networks are produced that surface the intellectual structure of the field, the main themes, and the relationships among major and influential research papers over time. We identify four areas in the intellectual structure of the field: conceptual modeling and databases; grammars and guidelines for conceptual modeling; requirements engineering and information systems design methodologies; and ontology constructs for conceptual modeling. Between 2017 and 2023, we distinguish nine research themes, including domain-specific conceptual modeling and applications, ontologies and applications, genomics, and datastores and multi-model data. The main path analysis identifies several trajectories among the major and most influential papers. This leads to insights into the lineage of key, influential papers in conceptual modeling research. The primordial nature of the main paths identified encompasses two important aspects. The first revolves around refining and complementing the entity-relationship model. The second identifies the contribution of ontologies for conceptual modeling to make the models more robust. Based on the findings from this bibliometric analysis, we propose several directions for future conceptual modeling research.
解读概念模型的基础和演变--知识结构、当前主题和发展轨迹
概念建模领域迄今已有五十多年的历史。为了了解这一领域是如何发展的,以及应该如何继续发展,我们有必要研究一下随着时间推移做出的贡献和出现的主题。在这项研究中,我们对从 1976 年到 2023 年的 4700 多篇研究论文进行了文献计量分析。我们先后应用了共引、书目耦合和主要路径分析。通过共引和引文网络,我们发现了该领域的知识结构、主要主题以及主要和有影响力的研究论文之间的关系。我们确定了该领域知识结构的四个方面:概念建模和数据库;概念建模的语法和指南;需求工程和信息系统设计方法;概念建模的本体构造。从 2017 年到 2023 年,我们将划分出九个研究主题,包括特定领域概念建模与应用、本体与应用、基因组学以及数据存储与多模型数据。主要路径分析确定了主要和最有影响力的论文之间的几条轨迹。这有助于深入了解概念建模研究中重要的、有影响力的论文的发展脉络。所确定的主要路径的原始性质包括两个重要方面。第一个方面是完善和补充实体关系模型。第二个方面是本体对概念建模的贡献,使模型更加稳健。根据文献计量分析的结果,我们提出了未来概念建模研究的几个方向。
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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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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