Earticle

초록

영어

A growing number of large scale knowledge graphs raises several issues how knowledge graph data can be organized, discovered, and integrated efficiently. We present a novel semantic-based mashup platform for contents convergence which consists of acquisition, RDF storage, ontology learning, and mashup subsystems. This platform servers a basis for developing other more sophisticated applications required in the area of knowledge big data. Moreover, this paper proposes an entity matching method using graph convolutional network techniques as a preliminary work for automatic classification and discovery on knowledge big data. Using real DBP15K and SRPRS datasets, the performance of our method is compared with some existing entity matching methods. The experimental results show that the proposed method outperforms existing methods due to its ability to increase accuracy and reduce training time.

목차

Abstract
1. Introduction
2. Architecture
3. Detail Description of Our System
3.1 RDF Storage Subsystem
3.2 Ontology Learning Subsystem
3.3 Mashup Subsystem
4. GCN-based Entity Matching Method
5. Performance Evaluation
6. Conclusion
Acknowledgement
References

키워드

저자

• Yongju Lee [ Professor, School of Computer Science and Engineering, Kyungpook National University, Kore ] Corresponding Author
• Hongzhou Duan [ PhD Student, School of Computer Science and Engineering, Kyungpook National University, Korea ]
• Yuxiang Sun [ Doctor, Software Technology Research Center, Kyungpook National University, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380