ONTOAPP: AN ONTOLOG APPLICATION ON SOLVING SOME HETEROGENEOUS PROBLEMS OF HEALTHCARE INFORMATION SHARING AND INTEROPERABILITY

  • Authors:

  • Ching-Song Don Wei;Jiann-Gwo Doong;Peter A. Ng

  • 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

  • Venue:
  • Journal of Integrated Design & Process Science
  • Year:
  • 2011

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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.