mlsanityData to 14 Oct 2026

A Diversity-Aware Domain Development Methodology

arXiv 2208.13064 ↗First posted 27 Aug 2022cs.AI cs.DBcited by 4

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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

  1. 2021Stratified Data Integrationcited by 9
  2. 2021Object Recognition as Classification via Visual Propertiescited by 5
  3. 2022Visual Ground Truth Construction as Faceted Classificationcited by 4
  4. 2022Representation Heterogeneitycited by 3
  5. 2022LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemascited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.