Ontology validation for managers

Detalhes bibliográficos
Ano de defesa: 2014
Autor(a) principal: Sales, Tiago Prince
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal do Espírito Santo
BR
Mestrado em Informática
Centro Tecnológico
UFES
Programa de Pós-Graduação em Informática
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
004
Link de acesso: http://repositorio.ufes.br/handle/10/4273
Resumo: Ontology-driven conceptual modeling is the activity of capturing and formalizing how a community perceives a domain of interest, using modeling primitives inherited from a foundational ontology. OntoUML is an example of a language that supports such activity, whose design derives from the Unified Foundational Ontology (UFO). Ontologies, in the sense of reference conceptual models, are useful in many fields. They include model-driven development of software systems, development of knowledge-based application (in the context of Semantic Web), semantic interoperability between information systems, and evaluation of modeling languages, to cite some. Regardless of the application, the quality of an ontology is directly related the quality of the results. Ontology and conceptual model quality encompasses a vast range of criteria. The validation activity aims to improve the domain appropriateness of a model. This means to help improve modeler’s confidence in saying: “I built the right model for my domain”. This thesis presents a validation framework usable by “managers” of the ontology world, i.e. modelers that are not experts in validation, logics and formal methods. The framework contains techniques and tools to help modelers systematically improve the quality of their models without demanding costly learning requirements. We build our framework on two conceptual pillars: model simulation and anti-patterns.