Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2018, Article ID 1359174, 13 pages
https://doi.org/10.1155/2018/1359174
Research Article

Methodology for Automatic Ontology Generation Using Database Schema Information

1Artificial Intelligence and Information Architecture, Department of Computer Engineering Graduate School, Dankook University, Yongin, Republic of Korea
2Department of Software Science, Dankook University, Yongin, Republic of Korea

Correspondence should be addressed to Young B. Park; rk.ca.kooknad@krapby

Received 14 December 2017; Accepted 4 March 2018; Published 2 May 2018

Academic Editor: Jeongyeup Paek

Copyright © 2018 JungHyen An and Young B. Park. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An ontology is a model language that supports the functions to integrate conceptually distributed domain knowledge and infer relationships among the concepts. Ontologies are developed based on the target domain knowledge. As a result, methodologies to automatically generate an ontology from metadata that characterize the domain knowledge are becoming important. However, existing methodologies to automatically generate an ontology using metadata are required to generate the domain metadata in a predetermined template, and it is difficult to manage data that are increased on the ontology itself when the domain OWL (Ontology Web Language) individuals are continuously increased. The database schema has a feature of domain knowledge and provides structural functions to efficiently process the knowledge-based data. In this paper, we propose a methodology to automatically generate ontologies and manage the OWL individual through an interaction of the database and the ontology. We describe the automatic ontology generation process with example schema and demonstrate the effectiveness of the automatically generated ontology by comparing it with existing ontologies using the ontology quality score.