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The Theoretical Analysis of Multi-dividing Ontology Learning by Rademacher Vector

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Data Mining and Big Data (DMBD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1453))

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Abstract

Generally, to express the hierarchical and categorical relationship between concepts, the ontology structure is often expressed as a tree diagram. The multi-dividing ontology learning algorithm makes full use of the characteristics of the tree structure ontology graph, and then plays a role in the engineering field. The main contribution of this paper is to use the Rademacher vector method to perform theoretical analysis on the multi-dividing ontology learning algorithm and obtain the error bound estimate for U-statistic criterion. The main proof tricks are to use the skills of statistical learning theory and McDiarmid’s inequality.

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References

  1. Osman, I., Ben Yahia, S., Diallo, G.: Ontology integration: approaches and challenging issues. Inf. Fusion 71, 38–63 (2021)

    Article  Google Scholar 

  2. Akremi, H., Zghal, S.: DOF: a generic approach of domain ontology fuzzification. Front. Comp. Sci. 15(3), 1–12 (2020). https://doi.org/10.1007/s11704-020-9354-z

    Article  Google Scholar 

  3. Ben Elhadj, H., Sallabi, F., Henaien, A., et al.: Do-Care: a dynamic ontology reasoning based healthcare monitoring system. Future Gener. Comput. Syst. Int. J. Escience 118, 417–431 (2021)

    Article  Google Scholar 

  4. Das, S.K., Swain, A.K.: An ontology-based framework for decision support in assembly variant design. J. Comput. Inf. Sci. Eng. 21(2), 021007 (2021). https://doi.org/10.1115/1.4048127

    Article  Google Scholar 

  5. Dimassi, S., Demoly, F., Cruz, C., et al.: An ontology-based framework to formalize and represent 4D printing knowledge in design. Comput. Ind. 126, 103374 (2021). https://doi.org/10.1016/j.compind.2020.103374

    Article  Google Scholar 

  6. Norris, E., Hastings, J., Marques, M.M.: Why and how to engage expert stakeholders in ontology development: insights from social and behavioural sciences. J. Biomed. Semantics 12(1), 4, https://doi.org/10.1186/s13326-021-00240-6

  7. Hastings, J., Glauer, M., Memariani, A., Neuhaus, F., Mossakowski, T.: Learning chemistry: exploring the suitability of machine learning for the task of structure-based chemical ontology classification. J. Cheminformatics 13(1), 1–20 (2021). https://doi.org/10.1186/s13321-021-00500-8

    Article  Google Scholar 

  8. Martin-Lammerding, D., Cordoba, A., Astrain, J.J., et al.: An ontology-based system to collect WSN-UAS data effectively. IEEE Internet Things J. 8(5), 3636–3652 (2021)

    Article  Google Scholar 

  9. Yadav, U., Duhan, N.: MPP-MLO: multilevel parallel partitioning for efficiently matching large ontologies. J. Sci. Ind. Res. 80(3), 221–229 (2021)

    Google Scholar 

  10. Almendros-Jimenez, J.M., Becerra-Teron, A.: Discovery and diagnosis of wrong SPARQL queries with ontology and constraint reasoning. Expert Syst. Appl. 165, 113772 (2021). https://doi.org/10.1016/j.eswa.2020.113772

    Article  Google Scholar 

  11. Gao, W., Guirao, J.L.G., Basavanagoud, B., et al.: Partial multi-dividing ontology learning algorithm. Inf. Sci. 467, 35–58 (2018)

    Article  MathSciNet  Google Scholar 

  12. Gao, W., Farahani, M.R.: Generalization bounds and uniform bounds for multi-dividing ontology algorithms with convex ontology loss function. Comput. J. 60(9), 1289–1299 (2017)

    MathSciNet  Google Scholar 

  13. Zhu, L., Hua, G.: Theoretical perspective of multi-dividing ontology learning trick in two-sample setting. IEEE ACCESS 8, 220703–220709 (2020)

    Article  Google Scholar 

  14. Hájek, J.: Asymptotic normality of simple linear rank statistics under alternatives. Ann. Math. Stat. 39, 325–346 (1968)

    Article  MathSciNet  Google Scholar 

  15. Fuchs, M., Hornung, R., Boulesteix, A.L., et al.: On the asymptotic behaviour of the variance estimator of a \(U\)-statistic. J. Stat. Plann. Inference 209, 101–111 (2020)

    Article  MathSciNet  Google Scholar 

  16. Goyal, M., Kumar, N.: Nonparametric multiple sample scale testing using \(U\)-statistics. Commun. Stat.-Simulation Comput. 49(11), 3019–3027 (2020)

    Article  MathSciNet  Google Scholar 

  17. Benes, V., Hofer-Temmel, C., Last, G., et al.: Decorrelation of a class of Gibbs particle processes and asymptotic properties of \(U\)-statistics. J. Appl. Probab. 57(3), 928–955 (2020)

    Article  MathSciNet  Google Scholar 

  18. Bouzebda, S., Nemouchi, B.: Uniform consistency and uniform in bandwidth consistency for nonparametric regression estimates and conditional \(U\)-statistics involving functional data. J. Nonparametric Stat. 32(2), 452–509 (2020)

    Article  MathSciNet  Google Scholar 

  19. Privault, N., Serafin, G.: Normal approximation for sums of weighted \(U\)-statistics-application to Kolmogorov bounds in random subgraph counting. Bernoulli 26(1), 587–615 (2020)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This research is partially supported by Modern Education Technology Research Project in Jiangsu Province (No. 2019-R-75637).

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Authors and Affiliations

  1. School of Computer Engineering, Jiangsu University of Technology, Changzhou, 213001, China

    Linli Zhu

  2. School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China

    Wei Gao

Authors
  1. Linli Zhu
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  2. Wei Gao
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Corresponding author

Correspondence to Linli Zhu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. The University of Sydney, Sydney, Australia

    Albert Zomaya

  4. Guangzhou University, Guangzhou, China

    Hongyang Yan

  5. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

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Zhu, L., Gao, W. (2021). The Theoretical Analysis of Multi-dividing Ontology Learning by Rademacher Vector. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_2

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  • DOI: https://doi.org/10.1007/978-981-16-7476-1_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7475-4

  • Online ISBN: 978-981-16-7476-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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