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A uniform compact genetic algorithm for matching bibliographic ontologies

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Abstract

Digital Library (DL) is a source of inspiration for the standards and technologies on Semantic Web (SW), which is usually implemented by using bibliographic data. To address DL’s data heterogeneity problem, it is necessary to annotate the bibliographic data with semantic information, which requires the utilization of the bibliographic ontologies. In particular, a bibliographic ontology provides the domain knowledge by specifying the domain concepts and their relationships. However, due to human subjectivity, a concept in different bibliographic ontologies might be defined in different names, causing the data heterogeneity problem. To address this issue, it is necessary to find the mappings between bibliographic ontologies’ concepts, which is the so-called bibliographic ontology matching. In this paper, a Uniform Compact Genetic Algorithm (UCGA) is proposed to match the bibliographic ontologies, which employs the real-valued compact encoding mechanism to improve the algorithm’s performance, the Linearly Decreasing Virtual Population (LDVP) to trade-off the exploration and exploitation of the algorithm, and the local perturbation to enhance the convergence speed and avoid the algorithm’s premature convergence. In addition, by using the Uniform Probability Density Function (UPDF) and Uniform Cumulative Distribution Function (UCDF), the UCGA can greatly reduce the evolutionary time and memory consumption. The experiment uses the Biblio testing cases provided by the Ontology Alignment Evaluation Initiative (OAEI) to evaluate UCGA’s performance and the experimental results show that UCGA is both effective and efficient.

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Notes

  1. https://mccormickml.com/2016/04/12/googles-pretrained-word2vec-model-in-python/

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

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Acknowledgments

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 Scientific Research Foundation of Fujian University of Technology (No. GY-Z17162), the Science and Technology Planning Project in Fuzhou City (No. 2019-G-40) and the Foreign Cooperation Project in Fujian Province (No. 2019I0019).

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Authors and Affiliations

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

    Chao Jiang

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

    Chao Jiang

  3. Fujian Key Lab for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou, Fujian, 350118, China

    Xingsi Xue

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

    Xingsi Xue

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

    Xingsi Xue

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  1. Chao Jiang
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Correspondence to Xingsi Xue.

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Jiang, C., Xue, X. A uniform compact genetic algorithm for matching bibliographic ontologies. Appl Intell 51, 7517–7532 (2021). https://doi.org/10.1007/s10489-021-02208-6

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