Résumé
Conceptual modelling of product configuration is an essential step towards improving reuse and configuration knowledge sharing, systems interoperability, and people communication. Among several approaches proposed, ontology-based approaches are known to provide better support for the conceptualization of product configuration knowledge in terms of precision and expressiveness of reasoning and representation. This paper studies product configuration ontologies and presents a meta-model of concepts and their relationships that are used in those ontologies. The proposed meta-model consists of a generalisation hierarchy of configuration types, the compositional structure of a configurable product, the generalisation hierarchy of a component, and constraint types. The meta-model has been designed with the aim of first understanding the current state of product configuration ontologies and then extending it for integrating new concepts.
| langue originale | Anglais |
|---|---|
| titre | 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings |
| rédacteurs en chef | Alexander Felfernig, Lidia Fuentes, Jane Cleland-Huang, Wesley K.G. Assuncao, Wesley K.G. Assuncao, Clement Quinton, Jianmei Guo, Klaus Schmid, Marianne Huchard, Inmaculada Ayala, Jose Miguel Rojas, Viet-Man Le, Jose Miguel Horcas |
| Editeur | ACM Press |
| Pages | 166-173 |
| Nombre de pages | 8 |
| ISBN (Electronique) | 9781450392068 |
| Les DOIs | |
| Etat de la publication | Publié - 12 sept. 2022 |
| Evénement | 26th ACM International Systems and Software Product Line Conference, ASPLC 2022 - Graz, Autriche Durée: 12 sept. 2022 → 16 sept. 2022 |
Série de publications
| Nom | 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings |
|---|---|
| Volume | B |
Une conférence
| Une conférence | 26th ACM International Systems and Software Product Line Conference, ASPLC 2022 |
|---|---|
| Pays/Territoire | Autriche |
| La ville | Graz |
| période | 12/09/22 → 16/09/22 |
Financement
This research is partly funded by the EOS VeriLearn project (FNRS Grant O05518F-RG03).
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