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.
Standards and ontologies for manufacturing understand resources differently. Because of this heterogeneity, misunderstandings arise concerning the basic features that characterize them. The purpose of the paper is to investigate how to ontologically model resources with the goal of facilitating the development of knowledge representation models for manufacturing. By reviewing the literature, we discuss and compare three approaches for the representation of resources depending on whether they are conceived in connection to either processes, plans or goals. By addressing the advantages and shortcomings of each view, we present a unifying perspective to enable the modeling of resources in an integrated manner. In this way, the intended meanings of the used notions are harmonized and, as a result, one can facilitate multiple experts to interact e.g., via data sharing and/or data integration procedures. Differently, by keeping three separated views, there is no guarantee that data coming from different parties will share common meanings even if the same terms are used. By the end of the paper, we present a case study to show the application of our approach and to compare it with an existing ontology for manufacturing.
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