Ontology-based development of diagnostic intelligent systems

Development of problem-oriented intelligent systems for solving semistructured problems remains a topical problem. The solution efficiency of this problem can be increased by improving approaches (methodologies) or creating specialized software. The paper describes an approach for the ontology-based development of diagnostic intelligent systems (including knowledge bases). A model-driven approach is used as a methodological basis with a subsequent redefinition of the main stages, models, transformation rules and means of implementation. The proposed approach is used for development of diagnostic systems in petrochemistry. Diagnosis is considered as the definition of the object’s state, including: monitoring the state, finding a location and determining the causes of a failure, forecasting the state. The objects to be diagnosed are presented as a sequence (chain) of states, where the state is a set of parameters and their values at a given time. The features of the proposed approach implementation are the following: the use of ontology as a computation independent model that describes the features of the problem; the use of the Rule Visual Modeling Language to present the platform independent and platform dependent models; the use of C Language Integrated Production System as a programming language and a target platform. An example of the approach application by means of software prototypes called Knowledge Base Development System and Personal Knowledge Base Designer is described.