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An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies

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Abstract

To address the heterogeneity problem of sensor data, it is necessary to conduct the Sensor Ontology Matching (SOM) process to find the mappings among diverse sensor data with the same semantics connotation. Currently, many Multi-Objective Evolutionary Algorithms (MOEAs) have been used to match the ontologies, which aim at finding a set of solutions called Pareto Set (PS) in the Pareto Front (PF) to represent a set of trade-off proposals for different Decision Makers (DMs). Being inspired by the success of MOEA with Inverse Model (IM-MOEA) in solving complicated optimization problems, in this work, an Improved IM-MOEA (I-IM-MOEA)-based matching technique is further proposed to enhance the algorithm’s matching efficiency as well as the alignment’s quality. To overcome the drawback of IM-MOEA that has poor performance on irregular PF, an adjusted selection mechanism is employed to avert the massive reduction in non-domination solutions on irregular PF, a dynamic Reference Vectors (RVs) is used to decrease the computational resources and boost the efficiency of the algorithm, and a local search strategy is introduced to promote the results’ quality. The experiment employs the benchmark provided by Ontology Alignment Evaluation Initiative (OAEI) and three sensor ontologies to assess the performance of I-IM-MOEA, and the experimental results show that I-IM-MOEA is both effective and efficient.

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Notes

  1. http://marinemetadata.org/community/teams/cog

  2. https://mccormickml.com/2016/04/12/googles-pretrained-word2vec-model-inpython/

  3. http://oaei.ontologymatching.org/2016/benchmarks/index.html

  4. https://www.w3.org/2005/Incubator/ssn/

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Acknowledgements

This work is supported by the Natural Science Foundation of Fujian Province (No. 2020J01875), the Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (No. YQ20206), the National Natural Science Foundation of China (Nos. 61773415, 61801527 and 61103143).

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Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue, Chao Jiang, Haolin Wang & Guojun Mao

  2. Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue, Chao Jiang & Haolin Wang

  3. School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, 350118, Fujian, China

    Xingsi Xue, Chao Jiang, Haolin Wang & Guojun Mao

  4. Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China

    Xingsi Xue

  5. Department of Computer Science and Software Engineering, Swinburne University of Technology, John Street, Hawthorn, VIC, 3122, Australia

    Pei-Wei Tsai

  6. School of Network Engineering, Zhoukou Normal University, Zhoukou, Henan, 466001, China

    Hai Zhu

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  1. Xingsi Xue
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  2. Chao Jiang
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  3. Haolin Wang
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  4. Pei-Wei Tsai
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  5. Guojun Mao
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Contributions

GM and HZ took part in investigation; P-WT involved in formal analysis; CJ participated in writing—original draft preparation; XX took part in writing—review and editing; HW involved in visualization. All authors read and approved the final manuscript.

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Correspondence to Xingsi Xue.

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Communicated by Vicente Garcia Diaz.

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Xue, X., Jiang, C., Wang, H. et al. An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies. Soft Comput 25, 12227–12240 (2021). https://doi.org/10.1007/s00500-021-05895-y

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