International journal of advanced smart convergence

  • 제12권2호
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  • Pages.34-46
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  • 2023
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  • 2288-2847(pISSN)
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  • 2288-2855(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
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Semantic-based Mashup Platform for Contents Convergence

  • Yongju Lee (School of Computer Science and Engineering, Kyungpook National University) ;
  • Hongzhou Duan (School of Computer Science and Engineering, Kyungpook National University) ;
  • Yuxiang Sun (Software Technology Research Center, Kyungpook National University)
  • 투고 : 2023.03.14
  • 심사 : 2023.03.21
  • 발행 : 2023.06.30
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초록

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.

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과제정보

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2016R1D1A1B 02008553). This study was supported by the BK21 FOUR project (AI-driven Convergence Software Education Research Program) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (4199990214394)

참고문헌

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