Abstract
Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a convenient and standardised way to exchange data and to layer analytical applications over it. However, poor design of ontologies of high degree of mismatch between them and industrial data naturally lead to KGs of low quality that impede the adoption and scalability of industrial analytics. Indeed, such KGs substantially increase the training time of writing queries for users, consume high volume of storage for redundant information, and are hard to maintain and update. To address this problem we propose an ontology reshaping approach to transform ontologies into KG schemata that better reflect the underlying data and thus help to construct better KGs. In this poster we present a preliminary discussion of our on-going research, evaluate our approach with a rich set of SPARQL queries on real-world industry data at Bosch and discuss our findings.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
ur Rehman, M.H., et al.: The role of big data analytics in industrial internet of things. Future Gener. Comput. Syst. 99, 247–259 (2019)
Kagermann, H.: Change through digitization—value creation in the age of industry 4.0. In: Albach, H., Meffert, H., Pinkwart, A., Reichwald, R. (eds.) Management of Permanent Change, pp. 23–45. Springer, Wiesbaden (2015). https://doi.org/10.1007/978-3-658-05014-6_2
Zhou, B., Pychynski, T., Reischl, M., Mikut, R.: Comparison of machine learning approaches for time-series-based quality monitoring of resistance spot welding (RSW). Arch. Data Sci. Ser. A (First) 5(1), 13 (2018)
Horrocks, I., Giese, M., Kharlamov, E., Waaler, A.: Using semantic technology to tame the data variety challenge. IEEE Internet Comput. 20(6), 62–66 (2016)
Zhou, D., et al.: Enhancing knowledge graph generation with ontology reshaping - Bosch case. In: Groth, P., et al. (eds.) ESWC (Demos/Industry) 2022. LNCS, vol. 13384, pp. 299–302. Springer, Cham (2022)
Zou, X., A survey on application of knowledge graph. In: Journal of Physics: Conference Series, vol. 1487, p. 012016. IOP Publishing (2020)
Yahya, M., Towards generalized welding ontology in line with ISO and knowledge graph construction. In: Groth, P., et al. (eds.) ESWC (Posters & Demos) 2022. LNCS, vol. 13384, pp. 83–88. Springer, Cham (2022)
Soylu, A., et al.: OptiqueVQS: a visual query system over ontologies for industry. Seman. Web 9(5), 627–660 (2018)
Zhou, B., et al.: SemML: facilitating development of ML models for condition monitoring with semantics. J. Web Seman. 71, 100664 (2021)
Svetashova, Y., et al.: Ontology-enhanced machine learning: a BOSCH use case of welding quality monitoring. In: Pan, J.Z., et al. (eds.) ISWC 2020. LNCS, vol. 12507, pp. 531–550. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62466-8_33
Zhou, D., Zhou, B., Chen, J., Cheng, G., Kostylev, E.V., Kharlamov, E.: Towards ontology reshaping for KG generation with user-in-the-loop: applied to BOSCH welding. In: IJCKG (2021)
Svetashova, Y., Zhou, B., Schmid, S., Pychynski, T., Kharlamov, E.: SemML: reusable ML for condition monitoring in discrete manufacturing. In: ISWC (Demos/Industry), vol. 2721, pp. 213–218 (2020)
Doran, P., Ontology reuse via ontology modularisation. In: KnowledgeWeb PhD Symposium, vol. 2006. Citeseer (2006)
Zhang, X., Cheng, G., Qu, Y.: Ontology summarization based on RDF sentence graph. In: WWW 2007, pp. 707–716 (2007)
Pouriyeh, S., et al.: Ontology summarization: graph-based methods and beyond. Int. J. Semant. Comput. 13(2), 259–283 (2019)
Zhou, B., Pychynski, T., Reischl, M., Kharlamov, E., Mikut, R.: Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding. J. Intell. Manuf. 33(4), 1139–1163 (2022). https://doi.org/10.1007/s10845-021-01892-y
Zhou, B., Zhou, D., Chen, J., Svetashova, Y., Cheng, G., Kharlamov, E.: Scaling usability of ML analytics with knowledge graphs: exemplified with a Bosch welding case. In: IJCKG, pp. 54–63. ACM (2021)
Zhou, B., et al.: The data value quest: a holistic semantic approach at Bosch. In: Groth, P., et al. (eds.) ESWC (Demos/Industry) 2022. LNCS, vol. 13384, pp. 287–290. Springer, Cham (2022)
Acknowledgements
The work was partially supported by the H2020 projects Dome 4.0 (Grant Agreement No. 953163), OntoCommons (Grant Agreement No. 958371), and DataCloud (Grant Agreement No. 101016835) and the SIRIUS Centre, Norwegian Research Council project number 237898.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, Z. et al. (2022). Query-Based Industrial Analytics over Knowledge Graphs with Ontology Reshaping. In: Groth, P., et al. The Semantic Web: ESWC 2022 Satellite Events. ESWC 2022. Lecture Notes in Computer Science, vol 13384. Springer, Cham. https://doi.org/10.1007/978-3-031-11609-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-11609-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11608-7
Online ISBN: 978-3-031-11609-4
eBook Packages: Computer ScienceComputer Science (R0)