AN ENGINEERING MODEL BASED ON ONTOLOGY AND PROBABILISTIC CALCULATION TO SUPPORT THE DIAGNOSIS

• Autor(a) principal: Lopes, Luiz Fernando
• Data de Publicação: 2012
• Outros Autores: Gonçalves, Alexandre Leopoldo, Todesco, José Leomar
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Sistemas & Gestão
• Texto Completo: https://www.revistasg.uff.br/sg/article/view/V6N3A4
• topic: Knowledge Engineering; Knowledge Based Systems; Diagnosis; Ontology; Probabilistic Calculation; Knowledge Engineering; Knowledge Based Systems; Diagnosis; Ontology; Probabilistic Calculation; Engenharia do Conhecimento. Sistemas Baseados em Conhecimento. Diagnóstico. Ontologia. Cálculo Probabilístico.; Gestão da Tecnologia; Sistemas de Informação

The diagnosis, as knowledge-intensive task, is a complex process since there is a wide variety ofelements and circumstances to be considered for a decision-making. Uncertainty generated by the subjectivity,vagueness and/or lack of updated information exist in almost all stages and interfere for the safety and efficacyin the outcome. The data and useful information, when collected and treated appropriately, deriving fromdiagnosis accomplished and which remain latent (unobserved/asleep), can become a valuable source ofknowledge if associated with the experience and observation of the individual who uses them. The goal ofthis article is to propose a model of Knowledge Engineering that allows the creation of new knowledge tosupport the diagnosis process. The methods and techniques of Knowledge Engineering, used on this model tosupport the process are: CommonKADS, Ontology, Probabilistic Calculation and Discovery Systems Basedon Literature. Through the integration of these elements, the proposed model is applied to a didactic examplewhich allows evidence to be highlighted and analyzed through research literature as potential new knowledge.After the information of a new knowledge, the inference process is updated. It is concluded, therefore, thatthrough this research, the proposed model meets the requirements for the generation of new knowledge, andcontributes to the improvement of the diagnostic test.