Object Ontologies as a Priori Models for Logical-Probabilistic Machine Learning - D. N. Gavrilin; A.V. Mantsivoda (Известия Иркутского государственного университета: Серия "Математика")
Logical-probabilistic machine learning (LPML) is an AI method able to explicitly work with a priori knowledge represented in data models. This feature significantly complements traditional deep learning knowledge acquiring. Object ontologies are a promising example of such a priori models. They are an expanded logical analog of object oriented programming models. While forming the core of the bSystem platform, object ontologies allow solving the applied problems of high complexity, in particular, in the field of management. The combination of LPML and object ontologies is capable of solving the forecasting problems, the tasks of automated control, problem detection, decision making, and business process synthesis. The proximity of object ontologies to the LPML formalism due to the same semantic modeling background makes it possible to integrate them within a single hybrid formal system, which is presented in this paper. In the paper we introduce the approach to integration of these two formalisms and provide some algorithmic basis for the implementation of the resulting hybrid formalism on the bSystem platform.