Artificial Intelligence is developing using statistical and logical approaches. Considerable efforts have been devoted to combining logical and probabilistic methods in a single framework, which influenced the development of several formalisms and programming tools. Such formalisms allow representation and reasoning about uncertain knowledge. Uncertainty happens in many areas, like medicine, manufacturing, weather forecasts, prediction (of e.g. voting intentions, natural disaster), etc. Ontologies are machine-processable formalisms for knowledge representation. Their purpose is to describe objects according to domain of interests. This knowledge is used by (automated) reasoning systems for query answering. Probabilistic ontologies are obtained by adding a probabilistic interpretation to the constraints forming the ontology, and adapting corresponding reasoning methods to handle these probabilities. The RFEPO is an interdisciplinary project and aims at formulating unification and matching problems used in probabilistic ontology reasoning, and to search and compare algorithms for their solution. Additionally, when there is no algorithm for solving them, our project aims to study the algebraic structures of degrees induced by Turing and other algorithmic reducibilities.