Formation and Adaptation Methods in the Design and Development Data Ontology

Authors: Drozd O.V. Published: 02.04.2024
Published in issue: #1(146)/2024  

Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing  
Keywords: сustomized design, product data management, data ontology management


The paper considers general problems in managing the design and development data, subject to customized design and production. It presents main approaches to information integration of the product life cycle processes. Ontological approach recommended for use to ensure semantic compatibility at the level of describing the syntax and semantics of the design and development data is considered in detail. Within the framework of the ontological approach, methods of automatic generation and adaptation of the basic ontology in the design and development data are described in detail. The method of the data basic ontology automatic generation involves formation of the data object logical groups followed by determining the ontology object types, associations between them and optimizing the resulting structure of the basic data ontology. The data basic ontology adaptation method involves formation of logical groups of the new or changed data elements with subsequent assessment of the statistical dependence and semantic connections both between the logical groups and between the data ontology objects. Based on results in analyzing connections between the data ontology objects, knowledge statements are clarified or formed. Implementation of a prototype of the intelligent product data management system implementing the proposed methods in managing the design and development data ontology is briefly considered

The work was carried out with financial support The Council on Grants of the President of the Russian Federation for Young Scientists and Postgraduates (SP-133.2022.5)

Please cite this article in English as:

Drozd O.V. Formation and adaptation methods in the design and development data ontology. Herald of the Bauman Moscow State Technical University, Series Instrument Engineering, 2024, no. 1 (146), pp. 104--121 (in Russ.). EDN: HVWKWE


[1] Fang J., Wei X. A knowledge support approach for the preliminary design of platform-based products in Engineering-to-Order manufacturing. Adv. Eng. Inform., 2020, vol. 46, art. 101196. DOI: https://doi.org/10.1016/j.aei.2020.101196

[2] Willner O., Gosling J., Schonsleben P. Establishing a maturity model for design automation in sales-delivery processes of ETO products. Comput. Ind., 2016, vol. 82, pp. 57--68. DOI: https://doi.org/10.1016/j.compind.2016.05.003

[3] Cannas V.G., Gosling J. A decade of engineering-to-order (2010--2020): progress and emerging themes. Int. J. Prod. Econ., 2021, vol. 241, art. 108274. DOI: https://doi.org/10.1016/j.ijpe.2021.108274

[4] McKendry D.A., Whitfield R.I., Duffy A.H.B. Product lifecycle management implementation for high value Engineering to Order programmes: an informational perspective. J. Ind. Inf. Integration, 2022, vol. 26, art. 100264. DOI: https://doi.org/10.1016/j.jii.2021.100264

[5] Fraga A.L., Vegetti M., Leone H.P. Ontology-based solutions for interoperability among product lifecycle management systems: a systematic literature review. J. Ind. Inf. Integration, 2020, vol. 20, art. 100176. DOI: https://doi.org/10.1016/j.jii.2020.100176

[6] El Kadiri S., Kiritsis D. Ontologies in the context of product lifecycle management: state of the art literature review. Int. J. Prod. Res., 2015, vol. 53, no. 18, pp. 5657--5668. DOI: https://doi.org/10.1080/00207543.2015.1052155

[7] Castane G.G., Xiong H., Dong D., et al. An ontology for heterogeneous resources management interoperability and HPC in the cloud. Future Gener. Comput. Syst., 2018, vol. 88, pp. 373--384. DOI: https://doi.org/10.1016/j.future.2018.05.086

[8] He Y., Hao C., Wang Y., et al. An ontology-based method of knowledge modelling for remanufacturing process planning. J. Clean. Prod., 2020, vol. 258, art. 120952. DOI: https://doi.org/10.1016/j.jclepro.2020.120952

[9] Tebes G., Peppino D., Becker P., et al. Analyzing and documenting the systematic review results of software testing ontologies. Inf. Softw. Technol., 2020, vol. 123, art. 106298. DOI: https://doi.org/10.1016/j.infsof.2020.106298

[10] Ming Z., Sharma G., Allen J.K., et al. An ontology for representing knowledge of decision interactions in decision-based design. Comput. Ind., 2020, vol. 114, art. 103145. DOI: https://doi.org/10.1016/j.compind.2019.103145

[11] Yang L., Cormican K., Yu M. Ontology-based systems engineering: a state-of-the-art review. Comput. Ind., 2019, vol. 111, pp. 148--171. DOI: https://doi.org/10.1016/j.compind.2019.05.003

[12] Alobaidi M., Malik K.M., Hussain M. Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain. Comput. Methods Programs Biomed., 2018, vol. 165, pp. 117--128. DOI: https://doi.org/10.1016/j.cmpb.2018.08.010

[13] Malik K.M., Krishnamurthy M., Alobaidi M., et al. Automated domain-specific healthcare knowledge graph curation framework: subarachnoid hemorrhage as phenotype. Expert Syst. Appl., 2020, vol. 145, art. 113120. DOI: https://doi.org/10.1016/j.eswa.2019.113120

[14] McCrae J.P., Arcan M., Asooja K., et al. Domain adaptation for ontology localization. J. Web Semant., 2016, vol. 36, pp. 23--31. DOI: https://doi.org/10.1016/j.websem.2015.12.001

[15] Mendonca M., Perozo N., Aguilar J. Ontological emergence scheme in self-organized and emerging systems. Adv. Eng. Inform., 2020, vol. 44, art. 101045. DOI: https://doi.org/10.1016/j.aei.2020.101045

[16] Huang C., Cai H., Xu L., et al. Data-driven ontology generation and evolution towards intelligent service in manufacturing systems. Future Gener. Comput. Syst., 2019, vol. 101, pp. 197--207. DOI: https://doi.org/10.1016/j.future.2019.05.075

[17] Sellami Z., Camps V., Aussenac-Gilles N. DYNAMO-MAS: a multi-agent system for ontology evolution from text. J. Data Semant., 2013, vol. 2, no. 2-3, pp. 145--161. DOI: https://doi.org/10.1007/s13740-013-0025-1

[18] Zhang H., Marangoni Y.R., Wu Z. Depth corrected edge detection of magnetic data. IEEE Trans. Geosci. Remote Sens., 2019, vol. 57, iss. 12, pp. 9626--9632. DOI: https://doi.org/10.1109/TGRS.2019.2928041

[19] Martel S. Magnetic navigation control of microagents in the vascular network: challenges and strategies for endovascular magnetic navigation control of microscale drug delivery carriers. IEEE Control Syst., 2013, vol. 33, iss. 6, pp. 119--134. DOI: https://doi.org/10.1109/MCS.2013.2279477

[20] Wang F., Song Y., Dong L., et al. Magnetic anomalies of submarine pipeline based on theoretical calculation and actual measurement. IEEE Trans. Magn., 2019, vol. 55, iss. 4, art. 6500410. DOI: https://doi.org/10.1109/TMAG.2019.2898951

[21] Drozd O.V., Kapulin D.V., Chentsov S.V. Expert evaluation of models and approaches of information support for the design of microelectronic systems. Promyshlennye ASU i kontrollery [Industrial Automatic Control Systems and Controllers], 2019, no. 6, pp. 25--30 (in Russ.). DOI: https://doi.org/10.25791/asu.06.2019.679