Abstract
Domain models have a myriad of uses, including analysis, requirement specification, aiding decision-making processes, facilitating shared understanding between stakeholders, and other applications. However, existing approaches and methodological guidelines for building such models are often either non-existent, isolated, ad hoc, incomplete, or only address specific parts of the domain modeling process rather than the whole endeavour. All of this has raised concerns about the validity, quality, and utility of domain models produced by such approaches, and the maturity of the domain modeling field as a whole. This study helps to address these issues by proposing a novel hybrid theory- and data-driven domain modeling framework that integrates taxonomy construction and computer-aided text analytics to develop practitioner-oriented classification dictionaries. The proposed methodological framework extends and improves upon seven existing guidelines from multiple fields by unifying them into a single, holistic, systematic, and generalizable approach. The proposed framework achieved 13.5/16 in a quality evaluation and was successfully applied to build a large-scale classification dictionary for the video game development domain. These results demonstrate the capability of the new approach to construct high-quality dual-purpose ontological and analytical models in complex domains.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.13
A Unified Transdisciplinary Framework of Best Practice Guidelines for Domain Engineering and Classification Dictionary Construction
Domain models have a myriad of uses, including analysis, requirement specification, aiding decision-making processes, facilitating shared understanding between stakeholders, and other applications. However, existing approaches and methodological guidelines for building such models are often either non-existent, isolated, ad hoc, incomplete, or only address specific parts of the domain modeling process rather than the whole endeavour. All of this has raised concerns about the validity, quality, and utility of domain models produced by such approaches, and the maturity of the domain modeling field as a whole. This study helps to address these issues by proposing a novel hybrid theory- and data-driven domain modeling framework that integrates taxonomy construction and computer-aided text analytics to develop practitioner-oriented classification dictionaries. The proposed methodological framework extends and improves upon seven existing guidelines from multiple fields by unifying them into a single, holistic, systematic, and generalizable approach. The proposed framework achieved 13.5/16 in a quality evaluation and was successfully applied to build a large-scale classification dictionary for the video game development domain. These results demonstrate the capability of the new approach to construct high-quality dual-purpose ontological and analytical models in complex domains.
Recommended Citation
McKenzie, T., Hoermann, S., Lukosch, S. & Morales-Trujillo, M.(2026). A Unified Transdisciplinary Framework of Best Practice Guidelines for Domain Engineering and Classification Dictionary Construction. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.13