The integration of OntoClean in WebODE

Enterprises will only be interested in the use of ontologies if such ontologies are evaluated enough. Therefore, the development of ontology evaluation tools is a crucial matter. We have built the ODEClean module in the workbench for building ontologies named WebODE. ODEClean allows cleaning taxonomies following the OntoClean method, and WebODE provides technical support to the Methontology methodology for building ontologies. We approached the development of this module in two steps. Firstly, we have integrated the OntoClean method into the conceptualisation activity of Methontology. Secondly, we have designed and implemented ODEClean using a declarative approach for specifying the knowledge to be used on the evaluation. ODEClean uses: (a) the Top Level of Universals, (b) metaproperties based on philosophical notions, and (c) OntoClean evaluation axioms. The main advantage of this approach is that the system could easily allow the user relax or stress the evaluation of the taxonomy just selecting more or less meta-properties

Ontology translation approaches for interoperability: A case study with Protege-2000 and WebODE

We describe four ontology translation approaches that can be used to exchange ontologies between ontology tools and/or ontology languages. These approaches are analysed with regard to two main features: how they preserve the ontology semantics after the translation process (aka semantic or consequence preservation) and how they allow final users and ontology-based applications to understand the resulting ontology in the target format (aka pragmatic preservation). These approaches are illustrated with practical examples that show how they can be applied to achieve interoperability between the ontology tools Protege-2000 and WebODE

Results of Taxonomic Evaluation of RDF(S) and DAML+OIL Ontologies using RDF(S) and DAML+OIL Validation Tools and Ontology Platforms Import Services

Before using RDF(S) and DAML+OIL ontologies in Semantic Web applications, its content should be evaluated from a knowledge representation point of view. In recent years, some RDF(S) and DAML+OIL ‘checkers’, ‘validators’, and ‘parsers’ have been created and several ontology platforms are able to import RDF(S) and DAML+OIL ontologies. Two are the experiments presented in this paper. The first one reveals that the majority of RDF(S) and DAML+OIL parsers (Validating RDF Parser, RDF Validation Service, DAML Validator, and DAML+OIL Ontology Checker) do not detect taxonomic mistakes in ontologies implemented in such languages. So, if such ontologies are imported by ontology platforms, are they able to detect such problems? The second experiment presented in this paper reveals that the majority of the ontology platforms (OilEd, OntoEdit, Protégé-2000, and WebODE) only detect a few of mistakes in concept taxonomies before importing them

WebODE: An integrated workbench for ontology representation, reasoning, and exchange

We present WebODE as a scalable, integrated workbench for ontological engineering that eases the modelling of ontologies, the reasoning with ontologies and the exchange of ontologies with other ontology tools and ontology-based applications. We will first describe the WebODE's knowledge model. We will then describe its extensible architecture, focusing on the set of independent ontology development functionalities that are integrated in this framework, such as the Ontology Editor, the Axiom Builder, the OKBC-based inference engine, and the documentation and interoperability services

Ontological Engineering: What are Ontologies and How Can We Build Them?

Ontologies are formal, explicit specifications of shared conceptualizations. There is much literature on what they are, how they can be engineered and where they can be used inside applications. All these literature can be grouped under the term “Ontological Engineering,” which is defined as the set of activities that concern the ontology development process, the ontology lifecycle, the principles, methods and methodologies for building ontologies, and the tool suites and languages that support them. In this chapter we provide an overview of Ontological Engineering, describing the current trends, issues and problem

LOVMI : vers une méthode interactive pour la validation d'ontologies

International audienceLes méthodes de construction d'ontologies se sont fortement développées au travers du traitement automatique du langage et de l'intérêt croissant aux corpus de données volumineux, engendrant un effacement progressif des acteurs du domaine au profit du traitement des données du domaine. Cependant, quelle que soit la ressource utilisée, la validation des ontologies demeure une question centrale de l'ingénierie des connaissances. Elle s'articule autour de deux problématiques complémentaires : (1) la validation structurelle et (2) la validation sémantique (de l'adéquation au domaine modélisé). Dans le premier cas, de nombreuses méthodes ont vu le jour offrant des supports réalisant automatiquement les tâches de validation. A contrario, les méthodes pour la recherche du second cas sont encore peu nombreuses. Nous proposons dans cet article la méthode LOVMI, mise en oeuvre pour la validation structurelle et sémantique du module « facteurs sociaux et environnementaux des maladies psychiatriques » de notre ontologie ONTOPSYCHIA. Mots-clés : Ontologies, validation d'ontologies, évaluation d'ontologies, psychiatrie, facteurs sociaux et envi-ronnementaux

Integration of the DOLCE top-level ontology into the OntoSpec methodology

This report describes a new version of the OntoSpec methodology for ontology building. Defined by the LaRIA Knowledge Engineering Team (University of Picardie Jules Verne, Amiens, France), OntoSpec aims at helping builders to model ontological knowledge (upstream of formal representation). The methodology relies on a set of rigorously-defined modelling primitives and principles. Its application leads to the elaboration of a semi-informal ontology, which is independent of knowledge representation languages. We recently enriched the OntoSpec methodology by endowing it with a new resource, the DOLCE top-level ontology defined at the LOA (IST-CNR, Trento, Italy). The goal of this integration is to provide modellers with additional help in structuring application ontologies, while maintaining independence vis-\`{a}-vis formal representation languages. In this report, we first provide an overview of the OntoSpec methodology's general principles and then describe the DOLCE re-engineering process. A complete version of DOLCE-OS (i.e. a specification of DOLCE in the semi-informal OntoSpec language) is presented in an appendix

Methodologies, tools and languages for building ontologies. Where is their meeting point?

In this paper we review and compare the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them. Ontology technology is nowadays mature enough: many methodologies, tools and languages are already available. The future work in this field should be driven towards the creation of a common integrated workbench for ontology developers to facilitate ontology development, exchange, evaluation, evolution and management, to provide methodological support for these tasks, and translations to and from different ontology languages. This workbench should not be created from scratch, but instead integrating the technology components that are currently available