Introducing an ontology based framework for dynamic hazard identification

An automated hazard identification technique can substantially contribute to risk assessment efficiency. This work presents an effort to introduce a dynamic hazard identification technique, which can translate the event propagation scenario into a graphical representation with probabilistic interpretation of hazards. Expert knowledge based database structure and probabilistic data driven dynamics were implemented on an ontology-based intelligent platform. A simple demonstration utilizing semantic webbased Web Ontology Language (OWL) was transformed into the Probabilistic-OWL (PR-OWL) based Multi Entity Bayesian Network (MEBN), which was incorporated with prior probabilities, to produce Situation Specific Bayesian Networks (SSBN) referring to hazard probabilities. A generalized and detailed dynamic hazard scenario model was then developed based on this same framework following the proposed methodology. Two open-source software, Protégé and UnBBayes, were used to develop the models. Case studies with different operational and environmental scenarios were presented to demonstrate the applicability of the generic model. To verify the application, the ontology based hazard scenario model was implemented on 45 individual accidents (from the CSB Database) with different operational aspects. This model was further used for causality studies and hazard mitigation measures

Geração automática de ontologias probabilísticas a partir de um modelo UMP-ST

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2017.O URP-ST é uma metodologia baseada no processo unificado que orienta o engenheiro de ontologias durante a construção de ontologias probabilísticas por meio de uma série de etapas que englobam desde a modelagem até a realização de inferências. A etapa de modelagem é definida pelo UMP-ST, uma metodologia iterativa e incremental voltada para a maioria das tecnologias semânticas. Uma delas é o PR-OWL, uma linguagem para a representação do MEBN. A modelagem de ontologias probabilísticas a partir do UMP-ST utilizando MEBN/PR-OWL pode ser realizada no UnBBayes, um framework para a construção gráfica de modelos probabilísticos e a realização de raciocínio plausível. Apesar da orientação dada pelo UMP-ST, a modelagem de ontologias probabilísticas é uma tarefa penosa e repetitiva. Durante a implementação do modelo, é necessário a construção da ontologia a partir do zero utilizando um determinada tecnologia semântica, além da modelagem feita no UMP-ST. Uma integração apropriada que ajude o usuário a implementar a ontologia, tal como um estrutura intermediária, agilizaria e facilitaria a sua implementação. Esse trabalho propõe um plug-in Java para o UnBBayes com o objetivo de automatizar o mapeamento de uma ontologia modelada via UMP ST em um modelo MEBN, permitindo ao usuário realizar inferências probabilísticas em ontologias com representação de conhecimento com ou sem incerteza probabilística.The URP-ST is a methodology based on the unified process that guides the ontology engineer in how to design Probabilistic Onologies. The UMP-ST is an incremental and iterative approach that covers the modeling step related to the URP-ST. It is a general methodology for the majority of the existing semantic technologies which support uncertainty. One of them is the PR-OWL, a language for MEBN representation. The modeling of probabilistic ontologies from the UMP-ST using MEBN / PR-OWL can be performed in UnBBayes, a framework for building probabilistic graphical models and performing plausible reasoning. Despite the guidance given by the UMP-ST, the implementation of a PO is a painful and repetitive task. During the implementation of the model, it is necessary to build the ontology from the zero using a specific semantic technology, even if the user models the PO in UMP-ST. A proper integration that helps the user to implement the PO, such as an intermediate structure, would expedite and facilitate its implementation. This work presents an automatic way to generate POs using MEBN representation from the UMP-ST model by mapping the elements of both sides. This is an extension of the UMP-ST to generate POs to an specific formalism and it is developed as a Java plug-in for UnBBayes