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International Conference on Smart City and Informatization

iSCI 2019: Smart City and Informatization pp 268–278Cite as

Semantic Knowledge Based Graph Model in Smart Cities

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1122))

Abstract

In smart cities, pervasive IoT devices generate an elephantine amount of multi-source heterogeneous data. The semantics helps to explore such complex datasets and drive towards higher-level insights. Later, these high-level insights are transformed to develop interlinks and associations among diverse sources of the data which leads towards knowledge discovery in a smart city. This discovery when combines with the domain knowledge using ontology-based approaches develop concepts and perceptions which initiate decision making in complex environments. However, the ontology-based approaches come up with certain limitations including an incapability to transform semi-structured data into useful knowledge, issues in handling inconsistent data, and inability to process large-scale, multi-source, and complex data of smart cities. Therefore, in this paper, we proposed a Semantic Knowledge Based Graph (SKBG) model as a solution to overcomes these limitations. The SKBG model is particularly customized to a smart city environment and purely utilizes knowledge-based graphs to incorporate any type of domain knowledge by combining diversify domains as a unit. As a result, the model works fine with diverse domain knowledge, automatically classify heterogeneous data by using machine learning techniques, handle large knowledge databases and support intelligent semantic search algorithms in smart cities. Finally, the results are summarized in the form of a knowledge graph which gives a comprehensive insight into the data.

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References

  1. Ali, A., Qadir, J., Rasool, R.U., Sathiaseelan, A., Zwitter, A., Crowcroft, J.: Big data for development: applications and techniques. Big Data Anal. 1(1), 2 (2016). https://doi.org/10.1186/s41044-016-0002-4

    Article  Google Scholar 

  2. Altaf, W., Shahbaz, M., Guergachi, A.: Applications of association rule mining in health informatics: a survey. Artif. Intell. Rev. 47(3), 313–340 (2017). https://doi.org/10.1007/s10462-016-9483-9

    Article  Google Scholar 

  3. Bandaru, S., Ng, A.H., Deb, K.: Data mining methods for knowledge discovery in multi-objective optimization: part a - survey. Expert Syst. Appl. 70, 139–159 (2017). https://doi.org/10.1016/j.eswa.2016.10.015

    Article  Google Scholar 

  4. Consoli, S., et al.: Producing linked data for smart cities: the case of catania. Big Data Res. 7, 1–15 (2017). https://doi.org/10.1016/j.bdr.2016.10.001

    Article  Google Scholar 

  5. d’Aquin, M., Davies, J., Motta, E.: Smart cities’ data: challenges and opportunities for semantic technologies. IEEE Internet Comput. 19(6), 66–70 (2015). https://doi.org/10.1109/MIC.2015.130

    Article  Google Scholar 

  6. González-Vidal, A., Jiménez, F., Gómez-Skarmeta, A.F.: A methodology for energy multivariate time series forecasting in smart buildings based on feature selection. Energy Build. 196, 71–82 (2019). https://doi.org/10.1016/j.enbuild.2019.05.021

    Article  Google Scholar 

  7. Gyrard, A., Zimmermann, A., Sheth, A.: Building IoT-based applications for smart cities: how can ontology catalogs help? IEEE Internet Things J. 5(5), 3978–3990 (2018). https://doi.org/10.1109/JIOT.2018.2854278

    Article  Google Scholar 

  8. Huang, Y., Li, T., Luo, C., Fujita, H., Horng, S.J.: Matrix-based dynamic updating rough fuzzy approximations for data mining. Knowl.-Based Syst. 119, 273–283 (2017). https://doi.org/10.1016/j.knosys.2016.12.015

    Article  Google Scholar 

  9. Kaur, N., Aggarwal, H.: Query based approach for referrer field analysis of log data using web mining techniques for ontology improvement. Int. J. Inf. Technol. 10(1), 99–110 (2018). https://doi.org/10.1007/s41870-017-0063-2

    Article  Google Scholar 

  10. Lau, B.P.L., et al.: A survey of data fusion in smart city applications. Inf. Fusion 52, 357–374 (2019). https://doi.org/10.1016/j.inffus.2019.05.004

    Article  Google Scholar 

  11. Lepri, B., Antonelli, F., Pianesi, F., Pentland, A.: Making big data work: smart, sustainable, and safe cities. EPJ Data Sci. 4(1), 16 (2015). https://doi.org/10.1140/epjds/s13688-015-0050-4

    Article  Google Scholar 

  12. Li, J., et al.: Feature selection: a data perspective. ACM Comput. Surv. 50(6), 94:1–94:45 (2017). https://doi.org/10.1145/3136625

    Article  Google Scholar 

  13. Lin, H., Liu, G., Yan, Z.: Detection of application-layer tunnels with rules and machine learning. In: Wang, G., Feng, J., Bhuiyan, M.Z.A., Lu, R. (eds.) SpaCCS 2019. LNCS, vol. 11611, pp. 441–455. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24907-6_33

    Chapter  Google Scholar 

  14. Moustaka, V., Vakali, A., Anthopoulos, L.G.: A systematic review for smart city data analytics. ACM Comput. Surv. 51(5), 103:1–103:41 (2018). https://doi.org/10.1145/3239566

    Article  Google Scholar 

  15. Pouyanfar, S., Yang, Y., Chen, S.C., Shyu, M.L., Iyengar, S.S.: Multimedia big data analytics: a survey. ACM Comput. Surv. 51(1), 10:1–10:34 (2018). https://doi.org/10.1145/3150226

    Article  Google Scholar 

  16. Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl.-Based Syst. 89, 14–46 (2015). https://doi.org/10.1016/j.knosys.2015.06.015

    Article  Google Scholar 

  17. Rettinger, A., Lösch, U., Tresp, V., d’Amato, C., Fanizzi, N.: Mining the semantic web. Data Min. Knowl. Discov. 24(3), 613–662 (2012). https://doi.org/10.1007/s10618-012-0253-2

    Article  MathSciNet  MATH  Google Scholar 

  18. Ristoski, P., Paulheim, H.: Semantic web in data mining and knowledge discovery: a comprehensive survey. J. Web Semant. 36, 1–22 (2016). https://doi.org/10.1016/j.websem.2016.01.001

    Article  Google Scholar 

  19. Saggi, M.K., Jain, S.: A survey towards an integration of big data analytics to big insights for value-creation. Inf. Process. Manag. 54(5), 758–790 (2018). https://doi.org/10.1016/j.ipm.2018.01.010

    Article  Google Scholar 

  20. Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013). https://doi.org/10.1109/TKDE.2011.253

    Article  Google Scholar 

  21. Sànchez, D., Batet, M., Isern, D., Valls, A.: Ontology-based semantic similarity: a new feature-based approach. Expert Syst. Appl. 39(9), 7718–7728 (2012). https://doi.org/10.1016/j.eswa.2012.01.082

    Article  Google Scholar 

  22. Ullah, F., Habib, M.A., Farhan, M., Khalid, S., Durrani, M.Y., Jabbar, S.: Semantic interoperability for big-data in heterogeneous iot infrastructure for healthcare. Sustain. Cities Soc. 34, 90–96 (2017). https://doi.org/10.1016/j.scs.2017.06.010

    Article  Google Scholar 

  23. Vaduva, C., Georgescu, F.A., Datcu, M.: Understanding heterogeneous eo datasets: a framework for semantic representations. IEEE Access 6, 11184–11202 (2018). https://doi.org/10.1109/ACCESS.2018.2801032

    Article  Google Scholar 

  24. Wang, H., Xu, Z., Fujita, H., Liu, S.: Towards felicitous decision making: an overview on challenges and trends of big data. Inf. Sci. 367–368, 747–765 (2016). https://doi.org/10.1016/j.ins.2016.07.007

    Article  Google Scholar 

  25. Wang, H., Xu, Z., Pedrycz, W.: An overview on the roles of fuzzy set techniques in big data processing: trends, challenges and opportunities. Knowl.-Based Syst. 118, 15–30 (2017). https://doi.org/10.1016/j.knosys.2016.11.008

    Article  Google Scholar 

  26. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)

    Google Scholar 

  27. Xu, Y., Gao, W., Zeng, Q., Wang, G., Ren, J., Zhang, Y.: FABAC: a flexible fuzzy attribute-based access control mechanism. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, K.-K.R. (eds.) SpaCCS 2017. LNCS, vol. 10656, pp. 332–343. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-72389-1_27

    Chapter  Google Scholar 

  28. Xue, X., Liu, S.: Matching sensor ontologies through compact evolutionary tabu search algorithm. In: Wang, G., Chen, J., Yang, L.T. (eds.) SpaCCS 2018. LNCS, vol. 11342, pp. 115–124. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05345-1_9

    Chapter  Google Scholar 

  29. Zhang, Q., Yang, L.T., Chen, Z., Li, P.: A survey on deep learning for big data. Inf. Fusion 42, 146–157 (2018). https://doi.org/10.1016/j.inffus.2017.10.006

    Article  Google Scholar 

  30. Zhang, S., Boukamp, F., Teizer, J.: Ontology-based semantic modeling of construction safety knowledge: towards automated safety planning for job hazard analysis (JHA). Autom. Constr. 52, 29–41 (2015). https://doi.org/10.1016/j.autcon.2015.02.005

    Article  Google Scholar 

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61632009, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, and in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01.

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Authors and Affiliations

  1. School of Computer Science, Guangzhou University, Guangzhou, 510006, China

    Saqib Ali & Guojun Wang

  2. Department of Computer Science, University of Agriculture, Faisalabad, 38000, Pakistan

    Saqib Ali & Komal Fatima

  3. School of Computer Science and Engineering, Central South University, Changsha, 410083, China

    Pin Liu

Authors
  1. Saqib Ali
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  2. Guojun Wang
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  3. Komal Fatima
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  4. Pin Liu
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Corresponding author

Correspondence to Guojun Wang .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. University of Ottawa, Ottawa, ON, Canada

    Abdulmotaleb El Saddik

  3. Shanghai Jiao Tong University, Shanghai, China

    Xuejia Lai

  4. University of Murcia, Murcia, Spain

    Gregorio Martinez Perez

  5. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

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© 2019 Springer Nature Singapore Pte Ltd.

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Ali, S., Wang, G., Fatima, K., Liu, P. (2019). Semantic Knowledge Based Graph Model in Smart Cities. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, KK. (eds) Smart City and Informatization. iSCI 2019. Communications in Computer and Information Science, vol 1122. Springer, Singapore. https://doi.org/10.1007/978-981-15-1301-5_22

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  • DOI: https://doi.org/10.1007/978-981-15-1301-5_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1300-8

  • Online ISBN: 978-981-15-1301-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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