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Decision support systems have been widely applied in various sectors in which a vast of models and algorithms have been researched and developed based on knowledge graphs. Recently, Fuzzy Knowledge Graph (FKG) has overcome the challenge of handling uncertain, incomplete input data in decision support systems; yet still its performance could be improved through the integration of multiple data sources. This paper proposes a novel conceptual framework of FKG called FKGF based on multiple data sources. The experimental results based on multiple data sources in the healthcare sector (including data.world, kaggle, hospital, etc.) show that the proposed framework gives positive results when combining data from many different sources.

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A Novel Framework for Fuzzy Knowledge Graph Integration from Multiple Data Sources: Case Study in Healthcare

  • 会议论文

  • 发表日期：

    2025年

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  • Nguyen Hong Tan,
  • Phan Hung Khanh,
  • Cu Kim Long,
  • Pham Van Hai,
  • Tran Manh Tuan,
  • Pham Minh Chuan,
  • Ngo Duc Tam,
  • Le Hoang Son

摘要

Decision support systems have been widely applied in various sectors in which a vast of models and algorithms have been researched and developed based on knowledge graphs. Recently, Fuzzy Knowledge Graph (FKG) has overcome the challenge of handling uncertain, incomplete input data in decision support systems; yet still its performance could be improved through the integration of multiple data sources. This paper proposes a novel conceptual framework of FKG called FKGF based on multiple data sources. The experimental results based on multiple data sources in the healthcare sector (including data.world, kaggle, hospital, etc.) show that the proposed framework gives positive results when combining data from many different sources.