Affiliations: Department of Computer Information Systems, BMCC, City
University of New York New York City, NY, USA | Department of Information Management, China University
of Technology, Taiwan | Department of Computer Science, Indiana
University-Purdue University Fort Wayne Fort Wayne, IN, USA
Abstract: Knowledge representation using ontology has been proposed to
resolve the issues of semantic heterogeneity in data interoperability and data
sharing among heterogeneous systems because ontology is a powerful expressive
language to represent the required knowledge about data of a specific domain
and has been applied in many fields, such as Semantic Web, e-commerce and
information interoperability, etc. However, building ontology for information
source manually is not only hard and error-prone, but also very personal if
there is no common guideline. We had proposed a framework for data
interoperability using semantic Web service to resolve the semantic
heterogeneity in healthcare environment. We found that learning ontology from
existing information resources is a good solution to explicitly express the
semantics of an information source, but some semantics may be missing during
the ontology learning process. Since relational database is widely used for
storing source data, in this paper a new approach of learning OWL ontology from
a relational database and information embedded in an application is proposed.
In order to acquire the complete conceptual information, a group of learning
rules are used to obtain OWL ontology, including classes, properties, property
characteristics, cardinalities and instances. A semi-automatic application
called OntoApp that implemented all proposed learning rules has been developed
and tested; the results show promising in knowledge learning from information
source.
Keywords: ontology, ontology learning, OWL, relational database, relational model
