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Matching Sensor Ontologies Through Compact Evolutionary Tabu Search Algorithm

  • Xingsi Xue
  • Shijian Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11342)

Abstract

Although sensor ontologies are regarded as the solution to data heterogeneity on the Semantic Sensor Web (SSW), these sensor ontologies themselves introduce heterogeneity by defining the same entity with different names or in different ways. To solve this problem, it is necessary to determine the semantic identical entities between heterogeneous sensor ontologies, so-called sensor ontology matching. Due to the complexity of the sensor ontology matching process, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. To overcome the EA-based ontology matcher’s shortcomings, i.e. premature convergence, long runtime and huge memory consumption, this paper present a Compact Evolutionary Tabu Search algorithm (CETS) to efficiently match the sensor ontologies. The experiment utilizes Ontology Alignment Evaluation Initiative (OAEI)’s bibliographic benchmark and library track, and two pairs of real sensor ontologies test CETS’s performance. The experimental results show that CETS is both effective and efficient when matching ontologies with various scales and under different heterogeneous situations, and comparing with the state-of-the-art sensor ontology matching systems, CETS can significantly improve the ontology alignment’s quality.

Keywords

Semantic Sensor Web Sensor ontology matching Compact evolutionary algorithm Tabu search 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61503082), Natural Science Foundation of Fujian Province (Nos. 2016J05145 and 2017H0003), Scientific Research Foundation of Fujian University of Technology (Nos. GY-Z17162 and GY-Z15007, GY-Z160130 and GY-Z160138), Fujian Province Outstanding Young Scientific Researcher Training Project (No. GY-Z160149) and Project of Fujian Education Department Funds (JK2017029).

References

  1. 1.
    Fernandez, S., Marsa-Maestre, I., Velasco, J.R., Alarcos, B.: Ontology alignment architecture for semantic sensor web integration. Sensors 13(9), 12581–12604 (2013)CrossRefGoogle Scholar
  2. 2.
    Gulić, M., Vrdoljak, B., Banek, M.: CroMatcher: an ontology matching system based on automated weighted aggregation and iterative final alignment. Web Semant.: Sci. Serv. Agents World Wide Web 41, 50–71 (2016)CrossRefGoogle Scholar
  3. 3.
    Hand, D., Christen, P.: A note on using the F-measure for evaluating record linkage algorithms. Stat. Comput. 28(3), 539–547 (2018)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Huber, J., Sztyler, T., Noessner, J., Meilicke, C.: CODI: combinatorial optimization for data integration–results for OAEI 2011. Ontol. Matching 134 (2011)Google Scholar
  5. 5.
    Otero-Cerdeira, L., Rodríguez-Martínez, F.J., Gómez-Rodríguez, A.: Ontology matching: a literature review. Expert Syst. Appl. 42(2), 949–971 (2015)CrossRefGoogle Scholar
  6. 6.
    Smutnicki, C., Bożejko, W.: Tabu search and solution space analyses. The job shop case. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2017. LNCS, vol. 10671, pp. 383–391. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-74718-7_46CrossRefGoogle Scholar
  7. 7.
    Stojanovic, N., Bradley, R.M., Wilkinson, S., Kabuka, M.R., Shironoshita, E.P.: Web-based ontology alignment with the GeneTegra alignment tool. In: SIMBig, pp. 127–132 (2017)Google Scholar
  8. 8.
    Wei, T., Lu, Y., Chang, H., Zhou, Q., Bao, X.: A semantic approach for text clustering using WordNet and lexical chains. Expert Syst. Appl. 42(4), 2264–2275 (2015)CrossRefGoogle Scholar
  9. 9.
    Xu, P., Wang, Y., Cheng, L., Zang, T.: Alignment results of SOBOM for OAEI 2010. In: Proceedings of the 5th International Conference on Ontology Matching, vol. 689. pp. 203–211. CEUR-WS.org (2010)Google Scholar
  10. 10.
    Xue, X., Chen, J.: A preference-based multi-objective evolutionary algorithm for semiautomatic sensor ontology matching. Int. J. Swarm Intell. Res. (IJSIR) 9(2), 1–14 (2018)CrossRefGoogle Scholar
  11. 11.
    Xue, X., Pan, J.S.: A compact co-evolutionary algorithm for sensor ontology meta-matching. Knowl. Inf. Syst. 56(2), 335–353 (2018)CrossRefGoogle Scholar
  12. 12.
    Xue, X., Pan, J.S.: An overview on evolutionary algorithm based ontology matching. J. Inf. Hiding Multimed. Signal Process 9, 75–88 (2018)Google Scholar
  13. 13.
    Yeh, J.F., Chang, L.T., Liu, C.Y., Hsu, T.W.: Chinese spelling check based on N-gram and string matching algorithm. In: Proceedings of the 4th Workshop on Natural Language Processing Techniques for Educational Applications, NLPTEA 2017, pp. 35–38 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Information Science and EngineeringFujian University of TechnologyFuzhouChina
  2. 2.Intelligent Information Processing Research CenterFujian University of TechnologyFuzhouChina
  3. 3.Fujian Provincial Key Laboratory of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouChina
  4. 4.Fujian Key Lab for Automotive Electronics and Electric DriveFujian University of TechnologyFuzhouChina

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