Keywords:Semantic Interoperability, Emergent Semantics, Semantics in Distributed Systems, Ontology.

Keywords: Object Role Modeling, ORM, NIAM, Conceptual Modeling, Ontology, Unsatisfiability, Reasoning, Decidability, DogmaModeler

Keywords: Verbalization, Object Role Modeling, ORM, NIAM, Conceptual Modeling, Ontology, Formal ontology engineering, DOGMA, DogmaModeler, Business Rules

Keywords: Ontology, Conceptual data modeling, Context, Ontology tools, Reusability, DOGMA, DogmaModeler, ORM, ORM-ML.

Keywords: Customer Complaint Management, CRM, e-CRM, Ontology, Core Ontology, Customer Complaint Ontology, DOGMA, ORM, Multilingual Representation of Ontologies.

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. Our methodology aims to addresses several related issues, such as (a) the scalability of building and sharing ontologies; (b) the maximization of knowledge reusability; (c) the design and engineering process, that also simplifies building and managing ontologies; (d) the coexistence of several rule systems and ontology languages around a same ontology; and (e) the reconcile of the need to represent semantics independently from language with the need to create and use processes entirely rooted and described in (natural) language. We first define formal ontologies in a logic sense, i.e. as ''representationless'' mathematical objects that form the range of a classical interpretation mapping from a first order language (sometimes called a conceptual schema, and assumed to lexically represent an application), to a set of possible (''plausible'') conceptualizations of the real world domain. We then give a database-inspired ''view'' on implementations of ontologies seen as resources. Following common model-theoretic database practice we decompose such resources into ontology bases and into of their explicit so-called ontological commitments. Such architecture allows to make the latter (crucial) notion explicit as a separate layer, with concrete and dedicated services, mediating between the ontology base and the application instances that commit to the ontology. We claim it also leads to methodological approaches that naturally extend database modeling theory and practice, and so may in turn lead to scalable solutions for ontology-based systems. We discuss examples of the DOGMA implementation of the ontology base server and commitment server.

Keywords: Ontology, Conceptual data modeling, Context, Ontology tools, Reusability, DOGMA

Abstract.Unlike data models, the main fundamental asset of ontologies is the independency from a particular application's needs, i.e. relatively generic domain knowledge that can be used and reused among different kinds of applications. The first main topic of this paper is to discuss some independency aspects that help to understand the differences and similarities between ontologies and data models. The second main topic of this paper is to present an ontology engineering framework that increases the independency between an ontology and applications that use it. Our ontology engineering approach separates ''atomic'' conceptual relations and ''predicative'' domain rules. An ontology, defined in this way, consists of an ontology base that holds intuitive context-specific conceptual relations and a layer of ''relatively generic'' ontological commitments that hold the ontology rules. The latter may be seen as reusable knowledge components.

Keywords: Ontology, Conceptual data modeling, Context, Ontology tools, Reusability, DOGMA.

Abstract.The Internet creates a strong demand for standardized exchange not only of data itself but especially of data semantics, as this same internet increasingly becomes the carrier of e-business activity (e.g. using web services). One way to achieve this is in the form of communicating ''rich'' conceptual schemas. Conceptual modeling techniques such as EER, ORM and to some extent the UML have been developed in the past for building information systems. These techniques or suitable extensions can often also be used to design business applications at a conceptual level. In this paper we adopt the well-known CM technique of ORM, which has a rich complement of business rule specification, and develop ORM-ML, an XML-based markup language for ORM. Clearly domain modeling of this kind will be closely related to work on so-called ontologies and we will briefly discuss the analogies and differences, introducing methodological patterns for designing distributed business models. Since ORM schemas are typically saved as graphical files, we designed a textual representation as a marked-up document in ORM-ML so we can save these ORM schemas in a more machine exchangeable way that suits networked environments. Moreover, we can now write style sheets to convert such schemas into another syntax, e.g. pseudo natural language, a given rule engine's language, first order logic, a given rule engine's language. We give (in appendix) a complete formal definition (''grammar'') of ORM-ML as an XML Schema, a comprehensive description of all of ORM's business rules in ORM-ML syntax and present an algorithm to map ORM schemas into ORM-ML. We illustrate the concept on a number of examples.

Keywords:Ontology, Conceptual data modeling, Context, Ontology tools, Reusability, DOGMA

Abstract.The purpose thesis of this paper is to present and discuss the scalability and reusability capabilities of DOGMA, an ontology modeling approach. Ontologies are repositories of domain knowledge and essential for knowledge management in organizations and for achieving interoperation among information systems. In the DOGMA ontology server architecture, we implement ontologies as classical database resources separating the ''fact base'' from the constraints, rules, derivations etc. that commit an application to such a given ontology ''base''. This separation allows an increased degree of scalability and reusability for the activities of ontology building. These are key issues in the context of the so-called Semantic Web where very large numbers of partial ontologies will emerge.

Keywords: Ontology, Conceptual data modeling, Context, Ontology tools, Reusability, DOGMA

Abstract.This paper describes a semantic portal through which knowledge can be gathered, stored, secured and accessed by members of a certain community. In particular, this portal takes into account companies and research institutes participating in the E.U. funded thematic network called OntoWeb. Ontology-based annotation of information is a prerequisite in order to offer the possibility of knowledge retrieval and extraction. The usage of well-defined semantics allows for the knowledge exchange between different OntoWeb community members. Thus, members are able to publish annotated information on the web, which is then crawled by a syndicator and stored in the portal's knowledge base. The backbone of the portal architecture consists of a knowledge base in which the ontology and the instances are stored and maintained. In addition, ontology-boosted query mechanisms and presentation facilities are provided.

Keywords: Ontology, Conceptual data modeling, Context, Ontology tools, Reusability, DOGMA