Ontologies in current computer science parlance are computer based resources that represent agreed domain semantics. Unlike data models, the fundamental asset of ontologies is their relative independence of particular applications, i.e. an ontology consists of relatively generic knowledge that can be reused by different kinds of applications/tasks. The first part of this paper concerns some aspects that help to understand the differences and similarities between ontologies and data models. In the second part we present an ontology engineering framework that supports and favours the genericity of an ontology. We introduce the DOGMA ontology engineering approach that separates "atomic" conceptual relations from "predicative" domain rules. A DOGMA ontology consists of an ontology base that holds sets of intuitive context-specific conceptual relations and a layer of "relatively generic" ontological commitments that hold the domain rules. This constitutes what we shall call the double articulation of a DOGMA ontology 1 .

Abstract. This paper tackles two main disparities between conceptual data schemes and ontologies, which should be taken into account when (re)using conceptual data modeling techniques for building ontologies. Firstly, conceptual schemes are intended to be used during design phases and not at the runtime of applications, while ontologies are typically used and accessed at runtime. To handle this first difference, we define a conceptual markup language (ORM-ML) that allows to represent ORM conceptual diagrams in an open, textual syntax, so that ORM schemes can be shared, exchanged, and processed at the run-time of autonomous applications. Secondly, unlike ontologies that are supposed to hold application-independent domain knowledge, conceptual schemes were developed only for the use of an enterprise application(s), i.e. "in-house" usage. Hence, we present an ontology engineering-framework that enables reusing conceptual modeling approaches in modeling and representing ontologies. In this approach we prevent application-specific knowledge to enter or to be mixed with domain knowledge. To end, we present DogmaModeler: an ontology-engineering tool that implements the ideas presented in the paper.

The success of the semantic web depends largely on how well ontologies can be utilized and formulated. Interoperability between systems using different versions of the same ontology is essential, and this implies the need for a regulated derivation of materialized ontology views (which can be considered a modified version of an ontology). This chapter applies the formalisms for such a derivation process to a practical example, emphasizing the possibility for automation, and also for optimization, to develop a high-quality derived ontology.

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Abstract. This paper presents a specifically database-inspired approach (called DOGMA) for engineering formal ontologies, implemented as shared resources used to express agreed formal semantics for a real world domain. We address several related key issues, such as knowledge reusability and shareability, scalability of the ontology engineering process and methodology, efficient and effective ontology storage and management, and coexistence of heterogeneous rule systems that surround an ontology mediating between it and application agents. Ontologies should represent a domain's semantics independently from "language", while any process that creates elements of such an ontology must be entirely rooted in some (natural) language, and any use of it will necessarily be through a (in general an agent's computer) language. To achieve the claims stated, we explicitly decompose ontological resources into ontology bases in the form of simple binary facts called lexons and into socalled ontological commitments in the form of description rules and constraints. Ontology bases in a logic sense, become "representationless" mathematical objects which constitute the range of a classical interpretation mapping from a first order language, assumed to lexically represent the commitment or binding of an application or task to such an ontology base. Implementations of ontologies become database-like on-line resources in the model-theoretic sense. The resulting architecture allows to materialize the (crucial) notion of commitment as a separate layer of (software agent) services, mediating between the ontology base and those application instances that commit to the ontology. We claim it also leads to methodological approaches that naturally extend key aspects of database modeling theory and practice. We discuss examples of the prototype DOGMA implementation of the ontology base server and commitment server.