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Authors: Osama A. Mehdi ; Hamidah Ibrahim and Lilly Suriani Affendey

Affiliation: Faculty of Computer Science and Information Technology, Malaysia

ISBN: 978-989-758-035-2

Keyword(s): Schema Matching, Instance based Schema Matching, Google Similarity, Regular Expression.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Collaboration and e-Services ; Data Analytics ; Data Engineering ; Data Integrity ; Data Management and Quality ; Data Management for Analytics ; Databases and Data Security ; e-Business ; Enterprise Information Systems ; Information and Systems Security ; Information Integration ; Integration/Interoperability ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Symbolic Systems

Abstract: Schema matching is the task of identifying correspondences between schema attributes that exist in different schemas. A variety of approaches have been proposed to achieve the main goal of high-quality match results with respect to precision (P) and recall (R). However, these approaches are unable to achieve high quality match results, as most of these approaches treated the instances as string regardless the data types of the instances. As a consequence, this causes unidentified matches especially for attribute with numeric instances which further reduces the quality of match results. Therefore, effort still needs to be done to further improve the quality of the match results. In this paper, we propose a framework for addressing the problem of finding matches between schemas of semantically and syntactically related data. 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 expre ssion as pattern recognition. To demonstrate the accuracy of our framework, we conducted an experimental evaluation using real world data sets. The results show that our framework 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). (More)

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Paper citation in several formats:
A. Mehdi, O.; Ibrahim , H. and Affendey , L. (2014). Instance Based Schema Matching Framework Utilizing Google Similarity and Regular Expression.In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-035-2, pages 213-222. DOI: 10.5220/0004990102130222

@conference{data14,
author={Osama A. Mehdi. and Hamidah Ibrahim . and Lilly Suriani Affendey .},
title={Instance Based Schema Matching Framework Utilizing Google Similarity and Regular Expression},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2014},
pages={213-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004990102130222},
isbn={978-989-758-035-2},
}

TY - CONF

JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Instance Based Schema Matching Framework Utilizing Google Similarity and Regular Expression
SN - 978-989-758-035-2
AU - A. Mehdi, O.
AU - Ibrahim , H.
AU - Affendey , L.
PY - 2014
SP - 213
EP - 222
DO - 10.5220/0004990102130222

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