2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT) (2014)
July 7, 2014 to July 10, 2014
Knowledge representation models based on Fuzzy Description Logics (DLs) can provide a foundation for reasoning in intelligent learning environments. While basic DLs are suitable for expressing crisp concepts and binary relationships, Fuzzy DLs are capable of processing degrees of truth/completeness about vague or imprecise information. This paper tackles the issue of representing fuzzy classes using OWL2 in a dataset describing Performance Assessment Results of Students (PARS).
Ontologies, Semantic Web, Fuzzy sets, Fuzzy logic, Data models, Cognition
F. Badie, T. Soru and J. Lehmann, "A Fuzzy Knowledge Representation Model for Student Performance Assessment," 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT), Athens, Greece, 2014, pp. 539-540.