Journal of Intelligent Manufacturing

, Volume 17, Issue 6, pp 653–666 | Cite as

Advanced human–machine system for intelligent manufacturing

Some issues in employing ontologies for natural language processing
  • Raffaello Lepratti


The use of ontologies has gained more and more interest above all for the knowledge management, e.g. the exchange of professional “know-how”, as reported in various previous papers. Under the pressure of a turbulent international market situation enterprises stress the importance of innovation in manufacturing areas. For instance, due to the drastic growing automation degree of manufacturing systems an intuitive interaction form is required, which enables the shop-floor personnel an active participation to the production without specific technical background, as well as to capture and retrieve systematically knowledge contents arising from the interaction process.

The following contribution takes this topic into consideration and proposes an innovative ontology- based approach called ontological filtering system (OFS) based on methods and procedures to formalize natural language contents in a systematic way. By means of a so-called ontological network (ON) generic term forms used in the human–machine interaction (HMI) via natural language could be led back to a set of pre-defined terms. Thus, the ON consists, on the one hand, of a large number of generic natural language terms and, on the other hand, of a set of so-called key terms. The generic terms are defined, classified in semantic categories and chained together per semantic relations for a specific use in a particular domain of discourse. The key terms are used to build information on machine level and, therefore, have a formal definition. Through additional syntax roles and application-specific semantic constrains a systematic access and processing of natural language instructions is accomplished computationally. The proposed concepts have been set up and tested within an experimental testbed. The obtained results show a high system performance and encourage the research team to invest further efforts, in order to validate the system operational performances towards its industrial use at shop-floor level.


Natural language processing Human-machine interaction Ontologies Intelligent manufacturing Systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Berger, U., Lepratti, R., & Erbe, H. (2004). Human–robot collaboration in automated manufacturing. In Proceedings of the conference IFAC-MIM 2004, October, Athen, 2004.Google Scholar
  2. Bischoff, R. Kazi, A., & Seyfarth, M. (2002). The MORPHA style guide for icon-based programming. In Proceedings of the IEEE international workshop on robot and human interactive communication RoMan (pp. 482–487). Berlin.Google Scholar
  3. Chomsky, N. 1965Aspects of the theory of syntaxMassCambridgeGoogle Scholar
  4. Ducatel, K. Bogdanowicz, M. Scapolo, F. Leijten, J., & Burgelman, J.-C. (2001). ISTAG: Scenarios for ambient intelligence in 2010. User-friendly Information Society, Final Report, IPTS-Seville, Feb. 2001. ( Scholar
  5. Duineveld, A. J., Stoter, R., Weiden, M. R., Kenepa, B., & Benjamins, V. R. (1999). WonderTools? A comparative study of ontological engineering tools. In Proceedings of the 12th workshop on knowledge acquisition, modeling and management(KAW’99), October 16–21, Banff, Alberta, Canada.Google Scholar
  6. Fensel, D. 2004Ontologies: A silver bullet for knowledge management and electronic commerce2Springer VerlagBerlin-HeidelbergGoogle Scholar
  7. Frege, G. (1969). On sense and reference. In G. Frege, Funktion, Begriff, Bedeutung (pp. 40–65). (1892) re-printed In: G. Patzig (Ed.), Göttingen.Google Scholar
  8. Goossenaerts, J. B. M., Arai, E., Shirase, K., Mills, J. J., & Kimura, F. (2002). Enhancing knowledge and skill chains in manufacturing and engineering. In Proceedings of DIISM working conference.Google Scholar
  9. Gruber, T.R. 1995Toward principles for the design of ontologies used for knowledge sharingInternational Journal of Human-Computer Studies43907928CrossRefGoogle Scholar
  10. Guarino, N. 1997Understanding, building and using ontologiesInternational Journal of Human-Computer Studies46290310CrossRefGoogle Scholar
  11. Gueting, R. H., & Dieker, S. (2003). Datenstrukturen und Algorithmen, Vol. 2. Stuttgart-Leipzig-Wiesbaden: Auflage, Teubner Verlag.Google Scholar
  12. IFAC TC 4.5 Delphi survey (2002). ifacdelphiresults.htmlGoogle Scholar
  13. Kornwachs, K. (2000). Data – Information – Knowledge: A trial of a technological enlightenment. “Věda, Technika, Společnost”, IX(XXII)/1, 5–27.Google Scholar
  14. Lay, K., Prassler, E., Dillmann, R., Grunwald, G., Hägele, M., Lawitzky, G., Stopp, A., & von Seelen, W. (2001). MORPHA: Communication and interaction with intelligent, anthropomorphic robot assistants. In Tagungsband Statustage Leitprojekte Mensch-Technik-Interaktion in der Wissensgesellschaft. Saarbrücken, Germany, Oktober 2001.Google Scholar
  15. Lepratti, R., & Berger, U. (2004). Ontology-based approach for the future European Scenario. In Proceedings of the third conference on management and control of production and logistics (pp. 123–128). IFAC/IEEE MCPL Conference 2004; November 03–05, Santiago, Chile.Google Scholar
  16. Lepratti, R., & Koeppel, P. (2005). Intercultural and interdisciplinary issues in computer-supported collaborative engineering. In Proceedings of the IFAC world congress. June, 2005, Prague.Google Scholar
  17. Lyons, J. 1977Semantics2Cambridge University PressNew YorkGoogle Scholar
  18. Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K. 1993Introduction to WordNet: An on-line lexical databaseStanford UniversityCalifornia, USAGoogle Scholar
  19. Noy, N.F., McGuinness, D.L. 2001Ontology development 101: A guide to creating your first ontologyStanford UniversityCalifornia, USAGoogle Scholar
  20. Sowa, J.F. 2000Knowledge representation: Logica, philosophical and computational foundationsBrooks & Cole (Ed.)CA USAGoogle Scholar
  21. Stopp, A., Horstmann, S., Kristensen, S., & Lohnert, F. (2002). Towards interactive learning for manufacturing assistants. In Proceedings of the 10th IEEE Inter. workshop on robot–human interactive communication. Paris, France.Google Scholar
  22. Ure, J., Dewar, R., Pooley, R., Lloyd, A., & Jaegersberg, J. (2004). Mental model as enablers of knowledge sharing and decision-making in the design of collaborative networked environments. In L. M. Camarinha-Matos, & H. Afsarmanesh (Eds.), Proceedings of the 5th IFIP working conference in virtual enterprises (PRO-VE 2004) (pp. 183-190). Toulose, France, August 23–26, Boston-London: Kluwert.Google Scholar
  23. Winograd, T. 1980What does it mean to understand language?Cognitive Science4209241CrossRefGoogle Scholar
  24. Woern, H. (2003). Tendenz in der Fabrikautomation und Robotik. Im: Im: Tagungsband der VDI-Konferenz ROBOTIK 2004 (S. 53–64). VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik, VDI-Berichte Nr. 1756.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  1. 1.Automation TechnologyBrandenburg University of Technology CottbusCottbusGermany

Personalised recommendations