Abstract:
Data model integration is an effective method to interoperate data that reside in different sources for the purpose of providing users with a single point of access to those data. Due to data heterogeneity, data correctness and consistency are significant for integration. Richer semantics of data is a major factor in resolving conflicts among heterogeneous data models. As object-oriented data model represents only schema-based semantics of data (e.g. classes, attributes, and class relationships), an alternative method such as ontology is useful for representing additional semantics (e.g. data values, data units, and synonym and hypernym lists). This thesis proposes a new approach to an ontology-based integration of data models, called Integrated Multiple Ontology approach, which provides a method for integrating two object-oriented data models by using an analysis of their ontologies. In this work, ontology will be used to describe semantics of data in each data model. Then the ontoloties are analysed and compared to determine their similarities and differences. The result of the comparison is used to devise an integrated ontology that will enable querying on the integrated information. This work is based on an assumption that the combination of a good knowledge representation that describes adequate semantics of the data model and a suitable integration algorithm leads to the correct and consistent integrated information system.