Search Advanced

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 4860-4870. doi: 10.3934/mbe.2021247
Previous Article Next Article
Research article Special Issues

Ternary compound ontology matching for cognitive green computing

  • 1.
    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
  • 2.
    School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
  • 3.
    School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
  • Received: 28 March 2021 Accepted: 31 May 2021 Published: 03 June 2021

  • Cognitive green computing (CGC) dedicates to study the designing, manufacturing, using and disposing of computers, servers and associated subsystems with minimal environmental damage. These solutions should provide efficient mechanisms for maximizing the efficiency of use of computing resources. Evolutionary algorithm (EA) is a well-known global search algorithm, which has been successfully used to solve various complex optimization problems. However, a run of population-based EA often requires huge memory consumption, which limited their applications in the memory-limited hardware. To overcome this drawback, in this work, we propose a compact EA (CEA) for the sake of CGC, whose compact encoding and evolving mechanism is able to significantly reduce the memory consumption. After that, we use it to address the ternary compound ontology matching problem. Six testing cases that consist of nine ontologies are used to test CEA's performance, and the experimental results show its effectiveness.

    Citation: Wei-Min Zheng, Qing-Wei Chai, Jie Zhang, Xingsi Xue. Ternary compound ontology matching for cognitive green computing[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4860-4870. doi: 10.3934/mbe.2021247

    Related Papers:

  • Cognitive green computing (CGC) dedicates to study the designing, manufacturing, using and disposing of computers, servers and associated subsystems with minimal environmental damage. These solutions should provide efficient mechanisms for maximizing the efficiency of use of computing resources. Evolutionary algorithm (EA) is a well-known global search algorithm, which has been successfully used to solve various complex optimization problems. However, a run of population-based EA often requires huge memory consumption, which limited their applications in the memory-limited hardware. To overcome this drawback, in this work, we propose a compact EA (CEA) for the sake of CGC, whose compact encoding and evolving mechanism is able to significantly reduce the memory consumption. After that, we use it to address the ternary compound ontology matching problem. Six testing cases that consist of nine ontologies are used to test CEA's performance, and the experimental results show its effectiveness.


    加载中


    [1] D. S. Modha, R. Ananthanarayanan, S. K. Esser, A. Ndirango, A. J. Sherbondy, R. Singh, Cognitive computing, Commun. ACM, 54 (2011), 62–71.
    [2] P. Kurp, Green computing, Commun. ACM, 51 (2008), 11–13.
    [3] M. Chen, F. Herrera, K. Hwang, Cognitive computing: architecture, technologies and intelligent applications, IEEE Access, 6 (2018), 19774–19783. doi: 10.1109/ACCESS.2018.2791469
    [4] S. Mirjalili, Genetic algorithm, in Evolutionary Algorithms and Neural Networks, Springer, 2019.
    [5] G. R. Harik, F. G. Lobo, D. E. Goldberg, The compact genetic algorithm, IEEE Trans. Evol. Comput., 3 (1999), 287–297. doi: 10.1109/4235.797971
    [6] N. Pour, A. Algergawy, R. Amini, D. Faria, I. Fundulaki, I. Harrow, et al., Results of the ontology alignment evaluation initiative 2020, Proceedings of the 15th International Workshop on Ontology Matching (OM 2020), 2020.
    [7] D. Oliveira, C. Pesquita, Improving the interoperability of biomedical ontologies with compound alignments, J. Biomed. Semantics, 9 (2018), 1–13. doi: 10.1186/s13326-017-0171-8
    [8] C. Pesquita, M. Cheatham, D. Faria, J. Barros, E. Santos, F. M. Couto, Building reference alignments for compound matching of multiple ontologies using obo cross-products, in OM, (2014), 172–173.
    [9] G. Acampora, U. Kaymak, V. Loia, A. Vitiello, Applying nsga-ii for solving the ontology alignment problem, in 2013 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, (2013), 1098–1103.
    [10] X. Xue, Y. Wang, Optimizing ontology alignments through a memetic algorithm using both matchfmeasure and unanimous improvement ratio, Artif. Intell., 223 (2015), 65–81. doi: 10.1016/j.artint.2015.03.001
    [11] M. Shamsfard, B. Helli, S. Babalou, Omega: Ontology matching enhanced by genetic algorithm. in 2016 Second International Conference on Web Research (ICWR), IEEE, (2016), 170–176.
    [12] Q. Lv, C. Jiang, H. Li, Solving ontology meta-matching problem through an evolutionary algorithm with approximate evaluation indicators and adaptive selection pressure, IEEE Access, 2020 (2020), 3046–3064.
    [13] N. Ferranti, S. S. R. F. Soares, J. F. de Souza, Metaheuristics-based ontology meta-matching approaches, Expert Syst. Appl., 173 (2021), 114578. doi: 10.1016/j.eswa.2021.114578
    [14] G. Acampora, V. Loia, A. Vitiello, Enhancing ontology alignment through a memetic aggregation of similarity measures, Inf. Sci., 250 (2013), 1–20. doi: 10.1016/j.ins.2013.06.052
    [15] X. Xue, Y. Wang, Using memetic algorithm for instance coreference resolution, IEEE Trans. Knowl. Data Eng., 28 (2015), 580–591.
    [16] O. Bodenreider, The unified medical language system (umls): integrating biomedical terminology, Nucleic Acids Res., 32 (2004), D267–D270. doi: 10.1093/nar/gkh061
    [17] X. Xue, J. S. Pan, An overview on evolutionary algorithm based ontology matching, J. Inf. Hiding Multimed. Signal Proc., 9 (2018), 75–88.
    [18] X. Xue, J. Zhang, Matching large-scale biomedical ontologies with central concept based partitioning algorithm and adaptive compact evolutionary algorithm, Appl. Soft Comput., 106 (2021), 107343. doi: 10.1016/j.asoc.2021.107343
    [19] I. Mott, Investigating semantic similarity for biomedical ontology alignment. Ph.D thesis, Universida De DeLisboa, 2017.
    [20] G. Kondrak, N-gram similarity and distance, in International symposium on string processing and information retrieval, Springer, 2005,115–126.
    [21] D. Sudholt, C. Witt, Update strength in edas and aco: How to avoid genetic drift, in Proceedings of the Genetic and Evolutionary Computation Conference 2016, 2016.
    [22] P. N. Robinson, S. Köhler, S. Bauer, D. Seelow, D. Horn, S. Mundlos, The human phenotype ontology: a tool for annotating and analyzing human hereditary disease, Am. J. Hum. Genet., 83 (2008), 610–615. doi: 10.1016/j.ajhg.2008.09.017
    [23] C. L. Smith, C. A. W. Goldsmith, J. T. Eppig, The mammalian phenotype ontology as a tool for annotating, analyzing and comparing phenotypic information, Genome Biol., 6 (2005), R7. doi: 10.1186/gb-2005-6-5-p7
    [24] G. V. Gkoutos, P. N. Schofield, R. Hoehndorf, The neurobehavior ontology: an ontology for annotation and integration of behavior and behavioral phenotypes, in Int. Rev. Neurobiol., 103 (2012), 69–87.
    [25] G. Schindelman, J. S. Fernandes, C. A. Bastiani, K. Yook, P. W Sternberg, Worm phenotype ontology: integrating phenotype data within and beyond the c. elegans community, BMC Bioinformatics, 12 (2011), 32. doi: 10.1186/1471-2105-12-32
    [26] G. V. Gkoutos, E. C. J. Green, A. M. Mallon, J. M. Hancock, D. Davidson, Using ontologies to describe mouse phenotypes, Genome Biol., 6 (2005), R8. doi: 10.1186/gb-2005-6-5-p8
    [27] J. Bard, S. Y. Rhee, M. Ashburner, An ontology for cell types, Genome Biol., 6 (2005), R21. doi: 10.1186/gb-2005-6-2-r21
    [28] C. Rosse, J. L. V. Mejino Jr, A reference ontology for biomedical informatics: the foundational model of anatomy, J. Biomed. Inf., 6 (2003), 478–500.
    [29] Gene Ontology Consortium, The gene ontology (go) database and informatics resource, Nucleic Acids Res., 32 (2004), D258–D261. doi: 10.1093/nar/gkh036
    [30] C. J. Mungall, C. Torniai, G. V. Gkoutos, S. E. Lewis, M. A. Haendel, Uberon, an integrative multi-species anatomy ontology, Genome Biol., 13 (2012), R5. doi: 10.1186/gb-2012-13-1-r5
    [31] C. J. Van Rijsbergen, Foundation of evaluation, J. Doc., 30 (1974), 365–373. doi: 10.1108/eb026584
    [32] J. M. V. Naya, M. M. Romero, J. P. Loureiro, C. R. Munteanu, A. P. Sierra, Improving ontology alignment through genetic algorithms, in Soft computing methods for practical environment solutions: Techniques and studies, (2010), 240–259.
    [33] G. Acampora, P. Avella, V. Loia, S. Salerno, A. Vitiello, Improving ontology alignment through memetic algorithms, in 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), IEEE, (2011), 1783–1790.
    [34] X. Xue, J. Chen, Matching biomedical ontologies through compact differential evolution algorithm with compact adaption schemes on control parameters, Neurocomputing, 2020 (2020), 1–9.
    [35] L. Schmetterer, E. L. Lehmann, Testing statistical hypotheses, Econometrica, 30 (1962), 462–465.
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Mathematical Biosciences and Engineering
3.9

Metrics

Article views(2372) PDF downloads(65) Cited by(1)

Preview PDF
Download XML
Export Citation
Article outline

Figures and Tables

Tables(5)

Other Articles By Authors

Related pages

Tools

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog