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Semantic Knowledge Based Graph Model in Smart Cities

  • Saqib Ali
  • Guojun WangEmail author
  • Komal Fatima
  • Pin Liu
Conference paper
Part of the Communications in Computer and Information Science book series (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.

Keywords

Smart cities Semantic Knowledge Based Graph model Semantic data mining Ontology-based approaches Linked data 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer ScienceGuangzhou UniversityGuangzhouChina
  2. 2.Department of Computer ScienceUniversity of AgricultureFaisalabadPakistan
  3. 3.School of Computer Science and EngineeringCentral South UniversityChangshaChina

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