Earticle

초록

영어

This paper proposes an ontology learning and population model from structured data sources. Recently various attempts have been made to harmonize Web 2.0 and the Semantic Web, named as Web 3.0 or Web 4.0. One of the most important issues for realization of the next Web platform is about how to make Web ontology rich as well as to address semantic interoperability between ontologies. To resolve those issues, Web ontology schemas should be precisely defined in semantic aspect and we should also develop a method for learning and population of ontology instances from diverse resources. This paper proposes a model which enables the ontology learning and population from structured data sources. The proposed model in this paper is a standard-based approach. In other words, our proposal is based on ISO/IEC 11179 - Metadata Registry, which is one of the international standards. This standard has been developed to support the interoperability between data by managing standardized common concepts. Therefore, our proposed model enhances the semantic interoperability between ontologies and enables the ontology enrichment from data sources.

목차

Abstract
 1. Introduction
 2. Proposed Model
  2.1. Framework
  2.2. System Architecture and Mapping Model
 3. System Implementation
  3.1. Prototyping Environment
  3.2. Implementation Results: Database/Ontology Schema Generation
  3.3. Implementation Results: Ontology Instance Population
 4. Evaluation
 5. Related Work
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Dongwon Jeong [ Dept. of Statistics & Computer Science, Kunsan National University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605