Advertisement

Towards an Upper Ontology and Hybrid Ontology Matching for Pervasive Environments

  • N. KarthikEmail author
  • V. S. Ananthanarayana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

Pervasive environments include sensors, actuators, handheld devices, set of protocols and services. The specialty of this environment is its power to manage with any device at any time anywhere and work autonomously for providing customized services to user. The different entities of pervasive environment collaborate with each other to accomplish an objective by sharing data among them. It raises an interesting problem called semantic heterogeneity. To address this problem, a hybrid ontology matching technique which combines direct and indirect matching techniques is proposed in this paper. To share and integrate data semantically, ontology matching technique establishes a semantic correspondence among various entities of pervasive application ontologies. To find the efficiency of proposed approach, we carried out set of experiments with real world pervasive applications. Experimental results prove that the proposed approach shows excellent performance in hybrid ontology matching. Results also proved that the use of background knowledge has influence over the performance of ontology matching technique.

Keywords

Ontology matching Pervasive environments Upper ontology 

References

  1. 1.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)CrossRefGoogle Scholar
  2. 2.
    Cerdeira, L.O.: Study and application of new methods for ontology matching. Dissertation, Universidade de Vigo (2014)Google Scholar
  3. 3.
    Abubakar, M., et al.: Instance-based ontology matching: a literature review. In: International Conference on Soft Computing and Data Mining. Springer, Cham (2018)Google Scholar
  4. 4.
    Wang, Z., Bie, R., Zhou, M.: Hybrid ontology matching for solving the heterogeneous problem of the IoT. In: IEEE TrustCom (2012)Google Scholar
  5. 5.
    He, W., Yang, X., Huang, D.: A hybrid approach for measuring semantic similarity between ontologies based on WordNet. In: International Conference on Knowledge Science, Engineering and Management. Springer, Heidelberg (2011)Google Scholar
  6. 6.
    Ducatel, G., Cui, Z. and Azvine, B.: Hybrid ontology and keyword matching indexing system. In: Proceedings of IntraWebs Workshop at WWW (2006)Google Scholar
  7. 7.
    Liu, X., Wang, Y., Zhu, S., Lin, H.: Combating web spam through trust-distrust propagation with confidence. Pattern Recognit. Lett. 34, 1462–1469 (2013)CrossRefGoogle Scholar
  8. 8.
    Wang, X., Su, J., Wang, B., Wang, G., Leung, H.F.: Trust description and propagation system: semantics and axiomatization. Knowl.-Based Syst. 90, 81–91 (2015)CrossRefGoogle Scholar
  9. 9.
    Jiang, C., Liu, S., Lin, Z., Zhao, G., Duan, R., Liang, K.: Domain-aware trust network extraction for trust propagation in large-scale heterogeneous trust networks. Knowl.-Based Syst. 111, 237–247 (2016)CrossRefGoogle Scholar
  10. 10.
    Wu, J., Xiong, R., Chiclana, F.: Uninorm trust propagation and aggregation methods for group decision making in social network with four tuple information. Knowl.-Based Syst. 96, 29–39 (2016)CrossRefGoogle Scholar
  11. 11.
    Xiong, F., Liu, Y., Cheng, J.: Modelling and predicting opinion formation with trust propagation in online social networks. Commun. Nonlinear Sci. Numer. Simul. 44, 513–524 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Karthik, N., Ananthanarayana, V.S.: Data trust model for event detection in wireless sensor networks using data correlation techniques. In: IEEE ICSCN (2017)Google Scholar
  13. 13.
    Karthik, N., Ananthanarayana, V.S.: A hybrid trust management scheme for wireless sensor networks. Wirel. Pers. Commun. 97(4), 5137–5170 (2017)CrossRefGoogle Scholar
  14. 14.
    Karthik, N., Ananthanarayana, V.S.: An ontology based trust framework for sensor-driven pervasive environment. In: 2017 Asia Modelling Symposium (AMS). IEEE (2017)Google Scholar
  15. 15.
    Karthik, N., Ananthanarayana, V.S.: A trust model for lightweight semantic annotation of sensor data in pervasive environment. In: 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS). IEEE (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information TechnologyNational Institute of Technology KarnatakaMangaloreIndia

Personalised recommendations