Examining the Adoption of Knowledge Graphs in the Manufacturing Industry: A Comprehensive Review
Author:
Martinez-Gil Jorge,Hoch Thomas,Pichler Mario,Heinzl Bernhard,Moser Bernhard,Kurniawan Kabul,Kiesling Elmar,Krause Franz
Abstract
AbstractThe integration of Knowledge Graphs (KGs) in the manufacturing industry can significantly enhance the efficiency and flexibility of production lines and improve product quality. By integrating and contextualizing information about devices, equipment, production resources, location, usage, and related data, KGs can be a powerful operational tool. Moreover, KGs can contribute to the intelligence of manufacturing processes by providing insights into the complex and competitive manufacturing landscape. This research work presents a comprehensive analysis of the current trends utilizing KG in the manufacturing sector. We provide an overview of the state of the art in KG applications in manufacturing and highlight the critical issues that need to be addressed to enable a successful implementation. Our research aims to contribute to advancing KG technology in manufacturing and realizing its full potential to enhance manufacturing operations and competitiveness.
Publisher
Springer Nature Switzerland
Reference46 articles.
1. Aggour, K.S., Kumar, V.S., Cuddihy, P., Williams, J.W., Gupta, V., Dial, L., Hanlon, T., Gambone, J., Vinciquerra, J.: Federated multimodal big data storage & analytics platform for additive manufacturing. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 1729–1738. IEEE, New York (2019) 2. Alam, M., Fensel, A., Martinez-Gil, J., Moser, B., Recupero, D.R., Sack, H.: Special issue on machine learning and knowledge graphs. Future Gener. Comput. Syst. 129, 50–53 (2022). https://doi.org/10.1016/j.future.2021.11.022 3. Bachhofner, S., Kiesling, E., Kurniawan, K., Sallinger, E., Waibel, P.: Knowledge graph modularization for cyber-physical production systems. In: Seneviratne, O., Pesquita, C., Sequeda, J., Etcheverry, L. (eds.) Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 20th International Semantic Web Conference (ISWC 2021), Virtual Conference, October 24–28, 2021. CEUR Workshop Proceedings, vol. 2980. CEUR-WS.org (2021). https://ceur-ws.org/Vol-2980/paper333.pdf 4. Bachhofner, S., Kiesling, E., Revoredo, K., Waibel, P., Polleres, A.: Automated process knowledge graph construction from BPMN models. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) Database and Expert Systems Applications—33rd International Conference, DEXA 2022, Vienna, Austria, August 22–24, 2022, Proceedings, Part I. Lecture Notes in Computer Science, vol. 13426, pp. 32–47. Springer, Berlin (2022). https://doi.org/10.1007/978-3-031-12423-5_3 5. Bachhofner, S., Kurniawan, K., Kiesling, E., Revoredo, K., Bayomie, D.: Knowledge graph supported machine parameterization for the injection moulding industry. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Sicilia, M., Martín-Moncunill, D. (eds.) Knowledge Graphs and Semantic Web—4th Iberoamerican Conference and Third Indo-American Conference, KGSWC 2022, Madrid, Spain, November 21–23, 2022, Proceedings. Communications in Computer and Information Science, vol. 1686, pp. 106–120. Springer, Berlin (2022). https://doi.org/10.1007/978-3-031-21422-6_8
|
|