Littérature scientifique sur le sujet « OntoClean »

Ontologia de intreprindere face o distincție clară între nivelul datalogic, infolog și esențial al tranzacțiilor cu blockchain și contractele inteligente. Metodologia OntoClean analizează ontologiile bazate pe proprietăți formale, independente de domenii ale claselor (metaproprietăți), fiind prima încercare de a formaliza noțiunile de analiză ontologică pentru sistemele informatice. Noțiunile sunt extrase din ontologia filosofică. În webul semantic, o proprietate este o relație binară. Distincția dintre proprietate și clasă este subtilă. Astfel, o metaproprietate este o proprietate a unei proprietăți sau a unei clase.

dolce, the first top-level (foundational) ontology to be axiomatized, has remained stable for twenty years and today is broadly used in a variety of domains. dolce is inspired by cognitive and linguistic considerations and aims to model a commonsense view of reality, like the one human beings exploit in everyday life in areas as diverse as socio-technical systems, manufacturing, financial transactions and cultural heritage. dolce clearly lists the ontological choices it is based upon, relies on philosophical principles, is richly formalized, and is built according to well-established ontological methodologies, e.g. OntoClean. Because of these features, it has inspired most of the existing top-level ontologies and has been used to develop or improve standards and public domain resources (e.g. CIDOC CRM, DBpedia and WordNet). Being a foundational ontology, dolce is not directly concerned with domain knowledge. Its purpose is to provide the general categories and relations needed to give a coherent view of reality, to integrate domain knowledge, and to mediate across domains. In these 20 years dolce has shown that applied ontologies can be stable and that interoperability across reference and domain ontologies is a reality. This paper briefly introduces the ontology and shows how to use it on a few modeling cases.

Depuis la fin des années 90, l'élaboration d'ontologies de domaines, de tâches et de méthodes, tôt dans la construction des modèles de résolution de problèmes, a été recommandée pour améliorer le processus d'ingénierie des connaissances. Toutefois, des limites pour intégrer ces différentes ontologies en un tout cohérent ont été identifiées, comme en témoignent notamment des débats sur le statut ontologique de la primitive "rôle de connaissance" dans le contexte de la méthode CommonKADS. C'est un tel verrou que nous proposons de lever avec la méthode OntoKADS, qui reprend, en les précisant sur un plan sémantique, les primitives de modélisation de CommonKADS. OntoKADS repose sur un cadre ontologique distinguant deux niveaux de modélisation - objet et méta - et intégrant, dans un tout cohérent, des entités d'un domaine (ex : des objets, états et processus) et des descriptions (ou conceptualisations) de ces entités (ex : des rôles, des tâches). Dans un tel cadre, les rôles de connaissance, notamment, sont assimilés à des descriptions d'entités d'un domaine, ces entités ne jouant qu'indirectement un rôle dans des raisonnements. La construction de modèles d'expertise en OntoKADS s'apparente dès lors principalement à une activité de construction d'une ontologie orientée résolution de problèmes. Pour supporter la méthode, un environnement logiciel a été développé et intégré dans la plate-forme de construction d'ontologies à partir de texte TERMINAE. Nous présentons dans le manuscrit une évaluation d'OntoKADS, d'une part sur un exemple pédagogique jouet (le diagnostic de pannes de voitures) puis sur une tâche de calage de codes de calculs, modélisée dans le cadre d'un projet RNTL
Since the late of 90's, the development of domains ontologies, tasks ontologies and methods ontologies, early in the construction of the problem-solving models, was recommended to improve the process of knowledge engineering. However, the limits to integrate these different ontologies into a coherent whole have been identified, as evidenced including debates on the ontological status of the "knowledge role" primitive in the context of the CommonKADS methodology. It is such a lock that we propose to raise with the OntoKADS methodology, which resumed the modeling primitives of CommonKADS, making them more specific on a semantic plan. OntoKADS relies on an ontological framework distinguishing two levels of modelling - object and meta - and integrating, into a coherent whole, domain entities (e. G. , objects, states and processes) and descriptions (or conceptualizations) of these entities (e. G. Roles, tasks). In such a context, the knowledge roles in particular are assimilated to descriptions of domain entities, these entities indirectly playing a role in reasonings. The construction of expertise models in OntoKADS is thus similar mainly to the construction of problem-solving oriented ontologies. To support the methodology, a softwareenvironment has been developed and integrated into the platform for building ontologies from texts, TERMINAE. We present in the manuscript an assessment of OntoKADS, on the one hand on a pedagogical example-toy (car diagnosis), then on a task of calibration modeled in the framework of a RNTL project

This paper studies the differences and similarities between domain ontologies and conceptual data models and the role that ontologies can play in establishing conceptual data models during the process of information systems development. A mapping algorithm has been proposed and embedded in a special purpose Transformation Engine to generate a conceptual data model from a given domain ontology. Both quantitative and qualitative methods have been adopted to critically evaluate this new approach. In addition, this paper focuses on evaluating the quality of the generated conceptual data model elements using Bunge-Wand-Weber and OntoClean ontologies. The results of this evaluation indicate that the generated conceptual data model provides a high degree of accuracy in identifying the substantial domain entities along with their relationships being derived from the consensual semantics of domain knowledge. The results are encouraging and support the potential role that this approach can take part in the process of information system development.

This article studies the differences and similarities between domain ontologies and conceptual data models and the role that ontologies can play in establishing conceptual data models during the process of developing information systems. A mapping algorithm has been proposed and embedded in a special purpose transformation engine to generate a conceptual data model from a given domain ontology. Both quantitative and qualitative methods have been adopted to critically evaluate this new approach. In addition, this article focuses on evaluating the quality of the generated conceptual data model elements using Bunge-Wand-Weber and OntoClean ontologies. The results of this evaluation indicate that the generated conceptual data model provides a high degree of accuracy in identifying the substantial domain entities, along with their relationships being derived from the consensual semantics of domain knowledge. The results are encouraging and support the potential role that this approach can take part in the process of information system development.