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A novel fuzzy knowledge graph structure for decision making of multimodal big data

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Abstract

Decision-making in the era of big data is always a challenge. Recently, various methods especially graph sampling have been presented to assist the decision more effectively. As real-world graphs are large, constantly evolving, and distributed in nature, it becomes necessary to sample their structures for many different goals. Therefore, acquiring a comprehensive and in-depth understanding of graph sampling is essential to strengthen this field. In addition, graph sampling techniques often rely on edge or vertex sampling without effective methods for rule or path sampling. In this paper, we propose a novel framework for the rule-based sampling method on fuzzy knowledge graphs. In this framework, fuzzy knowledge graphs are built on integrated databases from multiple sources. We design a purposive random sampling method based on fuzzy rules on graphs to prioritize important rules for output inference. The remaining important rules form the core structure of the fuzzy knowledge graph, known as the Fuzzy Knowledge Graph Structure (FKGS). This structure is considered as a compression mechanism to reduce computational complexity when representing and performing calculations for large-scale data problems. Experimental results based on benchmark datasets on diabetes mellitus show that the sampling method greatly reduces the calculation time while maintaining high accuracy. Moreover, the purposive random sampling method results in significantly higher accuracy than the random sampling method. Besides, the ANOVA method is also conducted to statistically validate the model. The results are significant for decision-making in the context of big data.

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We used publicly available datasets in the experiments that ensuring no privacy violations strengthens the manuscript.

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Acknowledgements

This research was funded by the research project QG.23.66 of Vietnam National University, Hanoi.

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Authors and Affiliations

  1. Graduate University of Science and Technology, Academy of Science and Technology, Hanoi, Vietnam

    Nguyen Hong Tan

  2. Faculty of Information Technology, Information Communication and Technology University (ICTU), Thai Nguyen, Vietnam

    Nguyen Hong Tan

  3. Information Technology Center, Ministry of Science and Technology (MOST), Hanoi, Vietnam

    Cu Kim Long

  4. Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam

    Tran Manh Tuan

  5. Faculty of Information Technology, Hung Yen University of Technology and Education, Hung Yen, Vietnam

    Pham Minh Chuan

  6. School of Information Communication Technology, Hanoi University of Science and Technology (HUST), Hanoi, Vietnam

    Pham Van Hai & Phan Hung Khanh

  7. Artificial Intelligence Research Center, VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam

    Nguyen Hong Tan & Le Hoang Son

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  1. Nguyen Hong Tan
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Contributions

N.H. Tan and P.H. Khanh are responsible for 1st draft writing and coding of the algorithms. C.K. Long and T.M. Tuan are responsible for data collection, data verification, and experiments. L.H. Son, PV. Hai and P.M.Chuan are responsible for methodology design, protocol perform, and revision of draft. All authors agree with this submission.

Corresponding author

Correspondence to Le Hoang Son.

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Ethical and informed consent for data used

The experimental data in this paper was conducted on publicly available datasets. The research ensures no violation of data privacy and security.

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The authors declare that they do not have any Conflict of interest.

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Appendix A: Source code

Appendix A: Source code

Source codes and datasets of the research can be found at this link:

https://github.com/Hoangthuhang1983/2024_Apin_Diabetes

https://github.com/Hoangthuhang1983/2024_Apin_Heart

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Tan, N.H., Long, C.K., Tuan, T.M. et al. A novel fuzzy knowledge graph structure for decision making of multimodal big data. Appl Intell 55, 490 (2025). https://doi.org/10.1007/s10489-025-06381-w

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