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Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base

Author

Listed:
  • Jiakang Xu

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
    Hubei HongShan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
    College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
    Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China)

  • Wolfgang Mayer

    (Industrial AI Research Centre, University of South Australia, Mawson Lakes, SA 5095, Australia)

  • Hongyu Zhang

    (Hubei HongShan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
    College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
    Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China)

  • Keqing He

    (School of Computer, Wuhan University, Wuhan 430072, China)

  • Zaiwen Feng

    (National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
    Hubei HongShan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
    College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
    Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan 430070, China)

Abstract

A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem ( Rel2Onto ). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we propose a novel method for semantically annotating structured data sources using machine learning, graph matching and modified frequent subgraph mining to amend the candidate model. In our work, Knowledge graph is used as prior knowledge. Our evaluation shows that our approach outperforms two state-of-the-art solutions in tricky cases where only a few semantic models are known.

Suggested Citation

  • Jiakang Xu & Wolfgang Mayer & Hongyu Zhang & Keqing He & Zaiwen Feng, 2022. "Automatic Semantic Modeling for Structural Data Source with the Prior Knowledge from Knowledge Base," Mathematics, MDPI, vol. 10(24), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4778-:d:1004903
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