Abstract
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.
Highlights
IntroductionSharing and integrating data from loosely coupled, heterogeneity of data representation and mapping data on different data sources are among serious problems in data integration [1,2,3,4]
The implementation of data integration still opens many problems to be solved
We claim that using OntoDI, the development of ontology knowledge gives simpler phases, complete steps, clear documentation for the ontology client and follow the standard of common ontology development phases proposed by Badr et al [18]
Summary
Sharing and integrating data from loosely coupled, heterogeneity of data representation and mapping data on different data sources are among serious problems in data integration [1,2,3,4]. Big data that most likely comprises of data heterogeneity produces data conflicts issues, especially on semantic aspects between different data representation and sources [3, 5,6,7]. The first problem is about data that have different names with the same meaning. Student‟s data is saved by pupil name and in another data source, student‟s data stored by the learner name This condition produces semantic data conflict between pupil and learner, because in these two data sources the same data about student information are stored
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
