# The Landscape of Ontology Reuse Approaches

arXiv: https://arxiv.org/abs/2011.12599 · HTML: https://mlsanity.com/paper/2011.12599/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [The Water Health Open Knowledge Graph](https://mlsanity.com/paper/2305.11051/) (2023)
- [The Music Meta Ontology: a flexible semantic model for the interoperability of music metadata](https://mlsanity.com/paper/2311.03942/) (2023)
- [Procedural Knowledge Ontology (PKO)](https://mlsanity.com/paper/2503.20634/) (2025)
- [Ontology Reuse: the Real Test of Ontological Design](https://mlsanity.com/paper/2205.02892/) (2022)
- [A Generative AI-driven Metadata Modelling Approach](https://mlsanity.com/paper/2501.04008/) (2024)
- [Extraction of common conceptual components from multiple ontologies](https://mlsanity.com/paper/2106.12831/) (2021)
- [GENOME: A GENeric methodology for Ontological Modelling of Epics](https://mlsanity.com/paper/2202.13751/) (2022)

## Abstract (arXiv)

Ontology reuse aims to foster interoperability and facilitate knowledge reuse. Several approaches are typically evaluated by ontology engineers when bootstrapping a new project. However, current practices are often motivated by subjective, case-by-case decisions, which hamper the definition of a recommended behaviour. In this chapter we argue that to date there are no effective solutions for supporting developers' decision-making process when deciding on an ontology reuse strategy. The objective is twofold: (i) to survey current approaches to ontology reuse, presenting motivations, strategies, benefits and limits, and (ii) to analyse two representative approaches and discuss their merits.