Research article Special Issues

Visual analysis of knowledge graph based on fuzzy sets in Chinese martial arts routines

  • Received: 11 February 2023 Revised: 18 May 2023 Accepted: 22 May 2023 Published: 31 May 2023
  • MSC : 03E72, 90B50, 90C31

  • There are many schools of Chinese martial arts routines and complex movements; research on this topic is more geared toward Taijiquan (a kind of traditional Chinese shadow boxing), which is a more well-known type of martial arts. Therefore, the purpose of this paper is to visually analyze the research of Chinese martial arts routines based on the knowledge graph method and to propose a knowledge graph method based on the fuzzy set theory, which is called the transF model throughout this paper. The transF model used the fuzzy relational operation of vectors to not only reduce the computational complexity, but to also better integrate multi-dimensional data, especially when the training set is not particularly sufficient. For the visual analysis of Chinese martial arts routines, this paper selected the 16-year data from 2005 to 2020 as the analysis sample, analyzed high-yield institutions and high-yield authors, and conducted a centrality analysis of the whole dataset. From the structure of the knowledge graph, traditional martial arts are the core part of Chinese martial arts, with a centrality of 0.14. Competitive martial arts are the main branch of Chinese martial arts and the third core after Tai Chi and traditional martial arts, with a centrality of 0.41, which is higher than that of traditional martial arts. This shows its importance in martial arts research.

    Citation: Jun Jiang, Junjie Lv, Muhammad Bilal Khan. Visual analysis of knowledge graph based on fuzzy sets in Chinese martial arts routines[J]. AIMS Mathematics, 2023, 8(8): 18491-18511. doi: 10.3934/math.2023940

    Related Papers:

  • There are many schools of Chinese martial arts routines and complex movements; research on this topic is more geared toward Taijiquan (a kind of traditional Chinese shadow boxing), which is a more well-known type of martial arts. Therefore, the purpose of this paper is to visually analyze the research of Chinese martial arts routines based on the knowledge graph method and to propose a knowledge graph method based on the fuzzy set theory, which is called the transF model throughout this paper. The transF model used the fuzzy relational operation of vectors to not only reduce the computational complexity, but to also better integrate multi-dimensional data, especially when the training set is not particularly sufficient. For the visual analysis of Chinese martial arts routines, this paper selected the 16-year data from 2005 to 2020 as the analysis sample, analyzed high-yield institutions and high-yield authors, and conducted a centrality analysis of the whole dataset. From the structure of the knowledge graph, traditional martial arts are the core part of Chinese martial arts, with a centrality of 0.14. Competitive martial arts are the main branch of Chinese martial arts and the third core after Tai Chi and traditional martial arts, with a centrality of 0.41, which is higher than that of traditional martial arts. This shows its importance in martial arts research.



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    [1] S. F. Li, F. G. Li, Evolvement and prospect of martial arts in the developing course of China, China Sport Sci., 33 (2013) 84–88.
    [2] Y. J. Han, H. B. Wang, H.M. Zhang, G. L. Cai, A test study on upper limbs' anaerobic metabolism ability of male martial arts routine players, Liaoning Sport Sci. Technol., 41 (2019), 37–41.
    [3] N. N. Sun, R. Y. Sun, S. H. Li, X. L. Wu, Martial arts routine difficulty action technology VR image target real-time extraction simulation, IEEE Access, 8 (2020), 155811–155818. https://doi.org/10.1109/ACCESS.2020.3014459 doi: 10.1109/ACCESS.2020.3014459
    [4] H. P. Gong, Influences of martial arts routine training on dynamic and static balance ability of university students, J. Guangzhou Sport Univ., 41 (2021), 75–78.
    [5] C. Wehner, C. Blank, M. Arvandi, C. Wehner, W. Schobersberger, Effect of Tai Chi on muscle strength, physical endurance, postural balance and flexibility: A systematic review and meta-analysis, BMJ Open Sport Exerc., 7 (2021). https://doi.org/10.1136/bmjsem-2020-000817 doi: 10.1136/bmjsem-2020-000817
    [6] W. Song, Y. P. Liu, L. N. Wang, Correlation analysis on competition results of high level chinese martial arts routine athletes, J. Beijing Sport Univ., 38 (2015), 135–140.
    [7] S. Z. Zheng, An analysis of the scientific knowledge map of the study of sports power in China, Sichuan Sports Sci., 40 (2021), 12–17.
    [8] Q. Wang, Z. G. Hu, Visualization analysis for the research fronts of the international Olympic movement, J. Xi'an Phys. Educ. Univ., 28 (2011), 433–436.
    [9] Z. Wang, Q. Liu, Knowledge map analysis in genetic research based on cite space, J. Southwest China Norm. Univ. (Natural Science Edition), 43 (2018), 143–150.
    [10] N. Y. Ji, Y. Gao, Y. B. Zhao, D. G. Yu, S. W. Chu, Knowledge graph assisted basketball sport news visualization, J. Comput.-Aided Des. Comput. Graph., 33 (2021), 837–846. https://doi.org/10.3724/SP.J.1089.2021.18590 doi: 10.3724/SP.J.1089.2021.18590
    [11] W. S. Chiu, T. C. M. Fan, S. B. Nam, P. H. Sun, Knowledge mapping and sustainable development of esports research: A bibliometric and visualized analysis, Sustainability, 13 (2021). https://doi.org/10.3390/su131810354 doi: 10.3390/su131810354
    [12] Z. L. Zhang, Z. F. Li, H. Liu, N. N. Xiong, Multi-scale dynamic convolutional network for knowledge graph embedding, IEEE T. Knowl. Data Eng., 34 (2022), 2335–2347. https://doi.org/10.1109/TKDE.2020.3005952 doi: 10.1109/TKDE.2020.3005952
    [13] Z. Li, Q. Zhang, F. Zhu, D. Li, C. Zheng, Y. Zhang, Knowledge graph representation learning with simplifying hierarchical feature propagation, Inform. Process. Manag., 60 (2023). https://doi.org/10.1016/j.ipm.2023.103348 doi: 10.1016/j.ipm.2023.103348
    [14] Z. F. Li, Y. Zhao, Y. Zhang, Z. L. Zhang, Multi-relational graph attention networks for knowledge graph completion, Knowl.-Based Syst., 25 (2022).
    [15] Z. X. Fang, S. Y. Wang, S. H. Li, Reconsideration of Chinese martial arts going global, J. Cap. Univ. Phys. Educ. Sports, 33 (2021), 564–569.
    [16] X. D. Wang, Challenges, opportunities and countermeasures of the international communication for Chinese martial arts culture under the background of ant globalization, J. Phys. Educ., 29 (2022), 20–24.
    [17] R. Kleminski, P. Kazienko, T. Kajdanowicz, Analysis of direct citation, co-citation and bibliographic coupling in scientific topic identification, J. Inform. Sci., 48 (2020), 349–373. https://doi.org/10.1177/0165551520962775 doi: 10.1177/0165551520962775
    [18] G. H. Feng, Y. X. Kong, Subject hotspot research based on word frequency analysis of time-weighted keywords, J. China Soc. Sci. Tech. Inform., 39 (2020), 100–110.
    [19] Z. G. Ma, R. Y. Ni, K. H. Yu, Recent advances, key techniques and future challenges of knowledge graph, Chinese J. Eng., 42 (2020), 1254–1266.
    [20] W. J. Chen, Y. Wen, X. Zhang, N. Zhang, S. Zhao, An improved TransE-based method for knowledge graph representation, Comput. Eng., 46 (2020), 63–69.
    [21] J. C. Figueroa-Garcia, M. A. Melgarejo-Rey, G. Hernandez-Perez, Representation of the Minkowski metric as a fuzzy set, Optim. Lett., 14 (2020), 395–408. https://doi.org/10.1007/s11590-018-1290-6 doi: 10.1007/s11590-018-1290-6
    [22] F. X. Wang, (U, V)-interval-valued fuzzy subsemigroups of semigroups, J. Chongqing Univ. Technol. (Natural Science), 3 (2019), 225–230.
    [23] L. Mao, M. Xie, Image semantic segmentation based on higher-order CRF model, Appl. Res. Comput., 30 (2013), 3514–3517.
    [24] Y. M. Li, X. Liang, Survey on canonical correlation analysis, J. China Univ. Metrol., 28 (2017), 113–118.
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