OntoDI: The Methodology for Ontology Development on Data Integration

The implementations of data integration in current days have many issues to be solved. Heterogeneity of data with non-standardization data, data conflicts between various data sources, data with a different representation, as well as semantic aspects problems are among the challenges and still open to research. Semantic data integration using ontology approach is considered as an appropriate solution to deal with semantic aspects problem in data integration. However, most methodologies for ontology development are developed to cover specific purpose and less suitable for common data integration implementation. This research offers an improved methodology for ontology development on data integration to deal with semantic aspects problem, called OntoDI. It is a continuation and improvement of the previous work about ontology development methods on agent system. OntoDI consists of three main parts, namely the pre-development, core-development and post-development, in which every part contains several phases. This paper describes the experiment of OntoDI in the electronic learning system domain. Using OntoDI, the development of ontology knowledge gives simpler phases, complete steps, and clear documentation for the ontology client. In addition, this ontology knowledge is also capable to overcome semantic aspect issues that happen in the sharing and integration process in education area.