Abstract
Every product goes through many stages of the life cycle from manufacturing through usage to utilization. Some stages can change its properties, which in turn changes its description. To describe the product and its life cycle many ontologies have been created with varying levels of detail. Ontologies usage at different product lifecycle management (PLM) stages provides a better match to these stages since they provide properties of the product important only for this stage. During the transition between stages, the data should be integrated across all systems in manufacturing domain to provide semantic interoperability. Therefore, an issue arises of matching descriptions presented with ontologies of lifecycle stages. This is especially critical if ontologies for different stages are created by various specialists (for example, designer, technology engineer, retailer, maintainer, etc.). The paper proposes the method of matching ontologies for the formation of a common PLM ontology based on the automatic matching of ontologies referred to PLM stages. It allows to overcome heterogeneity and ensure interoperability in the process of tracking the product through the PLM stages. The matching process is based on the identification of common concepts by which ontologies will be combined into one. To identify common concepts, the ontology matching method is used, based on a combination of a context-based matching with neural network to find similarities of concepts (name, characteristics names and their string values) and study the ontology structure to identify common design patterns.
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References
Mohd Ali, M., Rai, R., Otte, J.N., Smith, B.: A product life cycle ontology for additive manufacturing. Comput. Ind. 105, 191–203 (2019). https://doi.org/10.1016/j.compind.2018.12.007
Nyffenegger, F., Hänggi, R., Reisch, A.: A reference model for PLM in the area of digitization. In: Chiabert, P., Bouras, A., Noël, F., Ríos, J. (eds.) PLM 2018. IAICT, vol. 540, pp. 358–366. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01614-2_33
Teslya, N., Savosin, S.: Matching ontologies with Word2Vec-based neural network. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11619, pp. 745–756. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24289-3_55
Otero-Cerdeira, L., Rodríguez-Martínez, F.J., Gómez-Rodríguez, A.: Ontology matching: a literature review. Expert Syst. Appl. 42, 949–971 (2015). https://doi.org/10.1016/j.eswa.2014.08.032
Ngo, D., Bellahsene, Z.: YAM ++: a multi-strategy based approach for ontology matching task. In: ten Teije, A., et al. (eds.) EKAW 2012. LNCS (LNAI), vol. 7603, pp. 421–425. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33876-2_38
Schönteich, F., Kasten, A., Scherp, A.: A pattern-based core ontology for product lifecycle management based on DUL. In: CEUR Workshop Proceedings, pp. 92–106 (2018)
Bruno, G., Antonelli, D., Villa, A.: A reference ontology to support product lifecycle management. Procedia CIRP 33, 41–46 (2015). https://doi.org/10.1016/j.procir.2015.06.009
Euzenat, J., Shvaiko, P. (eds.): Ontology Matching. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38721-0_13
Raad, E., Evermann, J.: The role of analogy in ontology alignment: a study on LISA. Cogn. Syst. Res. 33, 1–16 (2015). https://doi.org/10.1016/j.cogsys.2014.09.001
Hecht, T., Buche, P., Dibie, J., Ibanescu, L., dos Santos, C.T.: Ontology alignment using web linked ontologies as background knowledge. In: Guillet, F., Pinaud, B., Venturini, G. (eds.) Advances in Knowledge Discovery and Management. SCI, vol. 665, pp. 207–227. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-45763-5_11
Lin, F., Sandkuhl, K.: A survey of exploiting WordNet in ontology matching. In: Bramer, M. (ed.) IFIP AI 2008. ITIFIP, vol. 276, pp. 341–350. Springer, Boston, MA (2008). https://doi.org/10.1007/978-0-387-09695-7_33
Manjula Shenoy, K., Shet, K.C., Dinesh Acharya, U.: NN based ontology mapping. In: Das, V.V., Chaba, Y. (eds.) Mobile Communication and Power Engineering. CCIS, vol. 296, pp. 122–127. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35864-7_18
Ganesh Kumar, S., Vivekanandan, K.: Odmm - an ontology based deep mining method to cluster the content from web servers. J. Theor. Appl. Inf. Technol. 74, 162–170 (2015)
Jayawardana, V., Lakmal, D., De Silva, N., Perera, A.S., Sugathadasa, K., Ayesha, B.: Deriving a representative vector for ontology classes with instance word vector embeddings. In: 7th International Conference on Innovative Computing Technology, INTECH 2017, pp. 79–84 (2017). https://doi.org/10.1109/INTECH.2017.8102426
Wohlgenannt, G., Minic, F.: Using word2vec to build a simple ontology learning system. In: CEUR Workshop Proceedings, pp. 2–5 (2016)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT 2019, pp. 4171–4186 (2019)
Hammar, K.: Ontology Design Patterns in WebProtégé. In: 14th International Semantic Web Conference (ISWC-2015), Betlehem, pp. 1–4 (2015)
Scharffe, F., Zamazal, O., Fensel, D.: Ontology alignment design patterns. Knowl. Inf. Syst. 40(1), 1–28 (2013). https://doi.org/10.1007/s10115-013-0633-y
Smirnov, A., Teslya, N., Savosin, S., Shilov, N.: Ontology matching for socio-cyberphysical systems: an approach based on background knowledge. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds.) NEW2AN/ruSMART/NsCC -2017. LNCS, vol. 10531, pp. 29–39. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67380-6_3
Levashova, T., Lundqvist, M., Sandkuhl, K., Smirnov, A.: Context-based modelling of information demand: approaches from information logistics and decision support. In: Proceedings of the 14th European Conference on Information Systems, ECIS 2006. 171 (2006)
Teslya, N., Smirnov, A., Levashova, T., Shilov, N.: Ontology for resource self-organisation in cyber-physical-social systems. In: Klinov, P., Mouromtsev, D. (eds.) KESW 2014. CCIS, vol. 468, pp. 184–195. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11716-4_16
Seigerroth, U., Kaidalova, J., Shilov, N., Kaczmarek, T.: Semantic web technologies in business and IT alignment: multi-model algorithm of ontology matching. In: AFIN 2013, The Fifth International Conference on Advances in Future Internet, pp. 50–56 (2013)
Sørensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Det Kongelige Danske Videnskabernes Selskab Biologiske Skrifter 5, 1–34 (1948)
Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw. 107, 3–11 (2018). https://doi.org/10.1016/j.neunet.2017.12.012
Acknowledgments
The reported study was funded by RFBR, project number 20-07-00904 for Sect. 3 of ontology matching methods and by Russian State Research No. 0073-2019-0005 in the other sections.
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Smirnov, A., Teslya, N. (2020). Ontology Matching for Product Lifecycle Management. In: Nyffenegger, F., Ríos, J., Rivest, L., Bouras, A. (eds) Product Lifecycle Management Enabling Smart X. PLM 2020. IFIP Advances in Information and Communication Technology, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-030-62807-9_21
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