Mariya Evtimova, Ivan Momtchev
Abstract: It is proposed a model of hybrid ontology that is case based, but is also suitable for big data as it is implemented rules. This ontology is divided into three parts- crisp part, fuzzy and probability part and big data part. Ontology use theory of the fuzzy sets with addition of probability logic for the realization of the fuzzy and probability part of the ontology. This hybrid ontology is suitable for vague and uncertain reasoning as it improve the quality of the returned results. Quality of the returned results is very important in the medical domain.
Key words: Big data, Case based reasoning, Fuzzy logic, Ontology, Probability logic
Abstract: Generation of adaptive tests by ontologies is a relatively new area of the research. More and more e-learning systems are based on ontologies, as the ontologies allow a clear conceptualization of knowledge in a given area and multiple reuse. This paper is dedicated to the description of the developed ontology for programming language C with the needs of the adaptive system for testing of students. The ontology is designed to automatically generate tests from specified field. It can be easily extended by new classes for more detailed description of the language C and merged with other ontologies and thus cover more subject area.
Key words: adaptive tests, ontology, programming language C.