A Diversity-Aware Domain Development Methodology
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Abstract (arXiv)
The development of domain ontological models, though being a mature research arena backed by well-established methodologies, still suffer from two key shortcomings. Firstly, the issues concerning the semantic persistency of ontology concepts and their flexible reuse in domain development employing existing approaches. Secondly, due to the difficulty in understanding and reusing top-level concepts in existing foundational ontologies, the obfuscation regarding the semantic nature of domain representations. The paper grounds the aforementioned shortcomings in representation diversity and proposes a three-fold solution - (i) a pipeline for rendering concepts reuse-ready, (ii) a first characterization of a minimalistic foundational knowledge model, named foundational teleology, semantically explicating foundational distinctions enforcing the static as well as dynamic nature of domain representations, and (iii) a flexible, reuse-native methodology for diversity-aware domain development exploiting solutions (i) and (ii). The preliminary work reported validates the potentiality of the solution components.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 39 references that are in the map. First posted on arXiv 27 Aug 2022.
Builds on
5 of 39 references.
References · 5
- 2021Stratified Data Integrationcited by 9
- 2021Object Recognition as Classification via Visual Propertiescited by 5
- 2022Visual Ground Truth Construction as Faceted Classificationcited by 4
- 2022Representation Heterogeneitycited by 3
- 2022LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemascited by 2
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2023Disentangling Domain Ontologiescited by 2
- 2024A Generative AI-driven Metadata Modelling Approachcited by 1
- 2025Information Ecosystem Reengineering via Public Sector Knowledge Representationcited by 0
- 2026What can Computer Vision learn from Ranganathan?cited by 0
- 2026What can Computer Vision learn from Ranganathan?30 Jan 2026
- 2025Information Ecosystem Reengineering via Public Sector Knowledge Representation21 Aug 2025
- 2024A Generative AI-driven Metadata Modelling Approach13 Dec 2024
- 2023Disentangling Domain Ontologies21 Mar 2023