A Framework to Normalize Ontology Representation for Stable Measurement
Yinglong Ma, Chunlong Wang, Beihong Jin · Journal of Computing and Information Science in Engineering · 2015
Ontology measurement is an important challenge in the field of knowledge management in order to manage the development of ontology based systems and reduce the risk of project failure. Effective ontology measurement is the precondition on which the meaningful and useful ontology evaluation can be made. We propose a framework to normalize representation of ontologies for their stable measurement, where the semantic enriched representation model (SERM) is proposed as the unique representation for ontologies. By the normalization framework, we provide a four-step procedure to extract ontology entities and calculate measures based on SERM model. Both the theoretical analysis and the experimental results show that our framework is effective and useful to perform stable ontology measurement. It is suitable to measure more expressive ontologies. This framework enables users to perform automatic ontology measurement without much expertise knowledge about ontology programming and reasoning.