Semantic Diagnostics of Smart Factories

  • Ognjen Savković
  • Evgeny KharlamovEmail author
  • Martin Ringsquandl
  • Guohui Xiao
  • Gulnar Mehdi
  • Elem Güzel Kalayc
  • Werner Nutt
  • Ian Horrocks
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11341)


Smart factories are one of the biggest trends in modern manufacturing, also known as Industry 4.0. They reach a new level of process automation and make heavy use of sensors in manufactoring equipment, which brings new challenges to monitoring and diagnostics at smart factories. We propose to address the challenges with a novel rule-based monitoring and diagnostics language that relies on ontologies and reasoning and allows one to write diagnostic tasks at a high level of abstraction. We show that our approach speeds up the diagnostic routine of engineers at Siemens: they can formulate and deploy diagnostic tasks in factories faster than with existing Siemens data-driven solutions. Moreover we show that our diagnostic language, despite the built-in reasoning, allows for efficient execution of diagnostic tasks over large volumes of industrial data. Finally, we implemented our ideas in a prototypical diagnostic system for smart factories.



This research is supported by the EPSRC projects MaSI\(^3\), DBOnto, ED\(^3\), and by the SIRIUS Centre, Norwegian Research Council project number 237898. Also it is partially supported by the Free University of Bozen-Bolzano projects QUEST, ROBAST and QUADRO.


  1. 1.
    Arenas, M., Grau, B.C., Kharlamov, E., Marciuska, S., Zheleznyakov, D.: Faceted search over ontology-enhanced RDF data. In: CIKM, pp. 939–948 (2014)Google Scholar
  2. 2.
    Arenas, M., Grau, B.C., Kharlamov, E., Marciuska, S., Zheleznyakov, D.: Faceted search over RDF-based knowledge graphs. J. Web Semant. 37–38, 55–74 (2016)CrossRefGoogle Scholar
  3. 3.
    Artale, A., Kontchakov, R., Ryzhikov, V., Zakharyaschev, M.: The complexity of clausal fragments of LTL. In: McMillan, K., Middeldorp, A., Voronkov, A. (eds.) LPAR 2013. LNCS, vol. 8312, pp. 35–52. Springer, Heidelberg (2013). Scholar
  4. 4.
    Artale, A., Kontchakov, R., Wolter, F., Zakharyaschev, M.: Temporal description logic for ontology-based data access. In: IJCAI 2013, pp. 711–717 (2013)Google Scholar
  5. 5.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, New York (2003)zbMATHGoogle Scholar
  6. 6.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: C-SPARQL: a continuous query language for RDF data streams. Int. J. Semant. Comput. 4(1), 3–25 (2010)CrossRefGoogle Scholar
  7. 7.
    Brandt, S., Kalaycı, E.G., Kontchakov, R., Ryzhikov, V., Xiao, G., Zakharyaschev, M.: Ontology-based data access with a Horn fragment of metric temporal logic. In: AAAI (2017)Google Scholar
  8. 8.
    Calvanese, D., et al.: Ontop: answering SPARQL queries over relational databases. Semant. Web 8(3), 471–487 (2017)CrossRefGoogle Scholar
  9. 9.
    Calvanese, D., et al.: The MASTRO system for ontology-based data access. Semant. Web 2(1), 43–53 (2011)Google Scholar
  10. 10.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: Tractable reasoning and efficient query answering in description logics: the DL-Lite family. JAR 39(3), 385–429 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Charron, B., Hirate, Y., Purcell, D., Rezk, M.: Extracting semantic information for e-commerce. In: Groth, P., et al. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 273–290. Springer, Cham (2016). Scholar
  12. 12.
    Corcho, O., Calbimonte, J.P., Jeung, H., Aberer, K.: Enabling query technologies for the semantic sensor web. Int. J. Semant. Web Inf. Syst. 8(1), 43–63 (2012)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    Jiménez-Ruiz, E., et al.: BootOX: practical mapping of RDBs to OWL 2. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 113–132. Springer, Cham (2015). Scholar
  15. 15.
    Kharlamov, E., et al.: Enabling semantic access to static and streaming distributed data with optique: demo. In: DEBS, pp. 350–353 (2016)Google Scholar
  16. 16.
    Kharlamov, E., et al.: Ontology-based integration of streaming and static relational data with optique. In: SIGMOD, pp. 2109–2112 (2016)Google Scholar
  17. 17.
    Kharlamov, E., Giacomelli, L., Sherkhonov, E., Grau, B.C., Kostylev, E.V., Horrocks, I.: Ranking, aggregation, and reachability in faceted search with semfacet. In: ISWC Posters & Demonstrations (2017)Google Scholar
  18. 18.
    Kharlamov, E., Giacomelli, L., Sherkhonov, E., Grau, B.C., Kostylev, E.V., Horrocks, I.: SemFacet: making hard faceted search easier. In: CIKM, pp. 2475–2478 (2017)Google Scholar
  19. 19.
    Kharlamov, E., et al.: Ontology based access to exploration data at statoil. In: ISWC, pp. 93–112 (2015)CrossRefGoogle Scholar
  20. 20.
    Kharlamov, E., et al.: Ontology based data access in statoil. J. Web Semant. 44, 3–36 (2017)CrossRefGoogle Scholar
  21. 21.
    Kharlamov, E., et al.: Optique: towards OBDA systems for industry. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 125–140. Springer, Heidelberg (2013). Scholar
  22. 22.
    Kharlamov, E., et al.: Semantic access to streaming and static data at Siemens. J. Web Semant. 44, 54–74 (2017)CrossRefGoogle Scholar
  23. 23.
    Kharlamov, E., et al.: A semantic approach to polystores. In: IEEE BigData, pp. 2565–2573 (2016)Google Scholar
  24. 24.
    Kharlamov, E., et al.: Diagnostics of trains with semantic diagnostics rules. In: Riguzzi, F., Bellodi, E., Zese, R. (eds.) ILP 2018. LNCS (LNAI), vol. 11105, pp. 54–71. Springer, Cham (2018). Scholar
  25. 25.
    Kharlamov, E., et al.: Semantic rules for machine diagnostics: execution and management. In: CIKM, pp. 2131–2134 (2017)Google Scholar
  26. 26.
    Kharlamov, E., et al.: How semantic technologies can enhance data access at siemens energy. ISWC 2014. LNCS, vol. 8796, pp. 601–619. Springer, Cham (2014). Scholar
  27. 27.
    Koymans, R.: Specifying real-time properties with metric temporal logic. Real-Time Syst. 2(4), 255–299 (1990)CrossRefGoogle Scholar
  28. 28.
    Mehdi, G., et al.: Semantic rule-based equipment diagnostics. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 314–333. Springer, Cham (2017). Scholar
  29. 29.
    Mehdi, G., et al.: SemDia: semantic rule-based equipment diagnostics tool. In: CIKM, pp. 2507–2510 (2017)Google Scholar
  30. 30.
    Pinkel, C., et al.: RODI: benchmarking relational-to-ontology mapping generation quality. Semant. Web 9(1), 25–52 (2018)CrossRefGoogle Scholar
  31. 31.
    Pinkel, C., et al.: IncMap: a journey towards ontology-based data integration. In: BTW, DBIS, pp. 145–164 (2017)Google Scholar
  32. 32.
    Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.: Linking data to ontologies. J. Data Semant. 10, 133–173 (2008)zbMATHGoogle Scholar
  33. 33.
    Savkovic, O., et al.: Theoretical characterization of signal diagnostic processing language. In: Description Logic Workshop (DL 2018), pp. 1–11 (2018)Google Scholar
  34. 34.
    Sherkhonov, E., Cuenca Grau, B., Kharlamov, E., Kostylev, E.V.: Semantic faceted search with aggregation and recursion. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10587, pp. 594–610. Springer, Cham (2017). Scholar
  35. 35.
    Soylu, A., Giese, M., Jiménez-Ruiz, E., Kharlamov, E., Zheleznyakov, D., Horrocks, I.: Ontology-based end-user visual query formulation: why, what, who, how, and which? Univers. Access Inf. Soc. 16(2), 435–467 (2017)CrossRefGoogle Scholar
  36. 36.
    Soylu, A., et al.: Querying industrial stream-temporal data: an ontology-based visual approach. JAISE 9(1), 77–95 (2017)MathSciNetGoogle Scholar
  37. 37.
    Soylu, A., et al.: OptiqueVQS: a visual query system over ontologies for industry. Semant. Web 9(5), 627–660 (2018)CrossRefGoogle Scholar
  38. 38.
    Vachtsevanos, G., Lewis, F.L., Roemer, M., Hess, A., Wu, B.: Intelligent Fault Diagnosis and Prognosis for Engineering Systems. Wiley, Hoboken (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ognjen Savković
    • 1
  • Evgeny Kharlamov
    • 2
    • 3
    Email author
  • Martin Ringsquandl
    • 4
    • 5
  • Guohui Xiao
    • 1
  • Gulnar Mehdi
    • 4
    • 6
  • Elem Güzel Kalayc
    • 1
  • Werner Nutt
    • 1
  • Ian Horrocks
    • 2
  1. 1.Free University of Bozen-BolzanoBolzanoItaly
  2. 2.University of OxfordOxfordUK
  3. 3.University of OsloOsloNorway
  4. 4.Siemens AG, Corporate TechnologyMunichGermany
  5. 5.Ludwig-Maximilians UniversityMunichGermany
  6. 6.Technical University of MunichMunichGermany

Personalised recommendations