The International Arab Journal of Information Technology (IAJIT)


An Approach for Instance Based Schema Matching

Instance based schema matching is the process of comparing instances from different heterogeneous data sources in determining the correspondences of schema attributes. It is a substitutional choice when schema information is not available or might be available but worthless to be used for matching purpose. Different strategies have been used by various instance based schema matching approaches for discovering correspondences between schema attributes. These strategies are neural network, machine learning, information theoretic discrepancy and rule based. Most of these approaches treated instances including instances with numeric values as strings which prevents discovering common patterns or performing statistical computation between the numeric instances. As a consequence, this causes unidentified matches especially for numeric instances. In this paper, we propose an approach that addresses the above limitation of the previous approaches. Since we only fully exploit the instances of the schemas for this task, we rely on strategies that combine the strength of Google as a web semantic and regular expression as pattern recognition. The results show that our approach is able to find 1-1 schema matches with high accuracy in the range of 93%-99% in terms of Precision (P), Recall (R), and F-measure (F). Furthermore, the results showed that our proposed approach outperformed the previous approaches although only a sample of instances is used instead of considering the whole instances during the process of instance based schema matching as used in the previous works.

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[31] Zaib K., Instance-Based Ontology Matching and the Evaluation of Matching Systems, PhD Dissertation, Dusseldorf University. Osama Mehdi received his Bachelor of Computer Science from the University of Babylon, Iraq in 2009 and M.Sc. by research degree in computer science and information technology from University Putra Malaysia, Malaysia in 2014. Currently, he is working as a lecturer at Al Mustaqbal College University. His research interests include Data Integration, Information Retrieval, Semantic Web, Pattern Recognition and Large-Scale Data Analysis (Big Data). Hamidah Ibrahim is currently a professor at the Faculty of Computer Science and Information Technology, Universiti Putra Malaysia. She obtained her PhD in computer science from the University of Wales Cardiff, UK in 1998. Her current research interests include databases (distributed, parallel, mobile, bio-medical, XML) focusing on issues related to integrity constraints checking, cache strategies, integration, access control, transaction processing, and query processing and optimization; data management in grid and knowledge-based systems. (e-mail: Lilly Affendey received her Bachelor of Computer Science from the University of Agriculture, Malaysia in 1991 and MSc in Computing from the University of Bradford, UK in 1994. In 2007 she received her PhD in Database Systems from University Putra Malaysia. Her research interests are in Multimedia Database, Content-based Video Retrieval and Big Data Analytics. She is currently an Associate Professor in University Putra Malaysia.