Ontology Development Through Concept Map and Text Analytics: The Case of Automotive Safety Ontology

  • Zirun Qi
  • Vijayan Sugumaran
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10859)


Ontology development is an expensive and time-consuming process. The development of real-world organizational ontology-based knowledge management systems is still in early stages. Some existing ontologies with simple tuples and properties are not designed for domain specific requirement, or does not utilize existing knowledge from organizational database or documents. Here we propose our concept map approach to first semi-automatically create a detailed level entities/concepts as a keyword list by applying natural language processing, including word dependency and POS tagging. Then this list can be used to extract entities/concepts for the same domain. This approach is applied to automotive safety domain. The results are further mapped to existing ontology and aggregated to form a concept map. We implement our approach in KNIME with Stanford NLP parser and generate a concept map from automotive safety complaint dataset. The final results expand the existing ontology, and also bridge the gap between ontology and real-world organization ontology-based knowledge management systems.


Ontology Concept map Text mining Automotive safety Knowledge management 


  1. 1.
    Kim, S., Suh, E., Hwang, H.: Building the knowledge map: an industrial case study. J. Knowl. Manag. 7(2), 34–45 (2003)CrossRefGoogle Scholar
  2. 2.
    Maedche, A., Motik, B., Stojanovic, L., Studer, R., Volz, R.: Ontologies for enterprise knowledge management. IEEE Intell. Syst. 18(2), 26–33 (2003)CrossRefGoogle Scholar
  3. 3.
    Vigo, M., Bail, S., Jay, C., Stevens, R.: Overcoming the pitfalls of ontology authoring: Strategies and implications for tool design. Int. J. Hum. Comput. Stud. 72, 835–845 (2014)CrossRefGoogle Scholar
  4. 4.
    Starr, R.R., de Oliveira, J.M.: Concept maps as the first step in an ontology construction method. Inf. Syst. 38(5), 771–783 (2013)CrossRefGoogle Scholar
  5. 5.
    Iqbal, R., Murad, M.A.A., Mustapha, A., Sharef, N.M.: An ontology development approach using concept maps by automatic term extraction. Int. J. Inf. Commun. Technol. 10(1), 51–65 (2017)Google Scholar
  6. 6.
    Novak, J. Canas, A.: The theory underlying concept maps and how to construct and use them. Technical Report. Institute for Human and Machine Cognition, Florida, 1-36 (2008)Google Scholar
  7. 7.
    Klyne, G. Carroll, J.J.: Resource description framework (RDF): concepts and abstract syntax (2006)Google Scholar
  8. 8.
    Cimiano, P., Völker, J.: Text2Onto. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005). Scholar
  9. 9.
    Gulla, J.A., Borch, H.O., Ingvaldsen, J.E.: Ontology Learning for Search Applications. In: Meersman, R., Tari, Z. (eds.) OTM 2007. LNCS, vol. 4803, pp. 1050–1062. Springer, Heidelberg (2007). Scholar
  10. 10.
    Haase, P., Völker, J.: Ontology learning and reasoning - dealing with uncertainty and inconsistency. In: Proceedings of the International Semantic Web Conference. Workshop 3: Uncertainty Reasoning for the Semantic Web (ISWC-URSW’05), pp. 45–55. Springer, Berlin, Heidelberg (2005)Google Scholar
  11. 11.
    Maedche, A. Staab, S.: Semi-automatic engineering of ontologies from text. In: Proceedings of the 12th Internal Conference on Software and Knowledge Engineering, pp. 231–239. Chicago (2000)Google Scholar
  12. 12.
    Navigli, R., Velardi, P.: Learning domain ontologies from document warehouses and dedicated web sites. Comput. Linguist. 30(2), 151–179 (2004)CrossRefGoogle Scholar
  13. 13.
    Sabou, M., Wroe, C., Goble, C., Stuckenschmidt, H.: Learning domain ontologies for semantic web service descriptions. Web Semant. Sci. Serv. Agents World Wide Web 3(4), 340–365 (2005)CrossRefGoogle Scholar
  14. 14.
    Grant, R.M.: Prospering in dynamically-competitive environments: organizational capability as knowledge integration. Knowledge and Strategy, pp. 133–153 (1999)Google Scholar
  15. 15.
    Motik, B.: On the properties of metamodeling in OWL. J. Logic Comput. 17(4), 617–637 (2007)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Nidumolu, S.R., Subramani, M., Aldrich, A.: Situated learning and the situated knowledge web: exploring the ground beneath knowledge management. J. Manag. Inf. Syst. 18(1), 115–151 (2001)CrossRefGoogle Scholar
  17. 17.
    Rus, M.: Lindvall.: knowledge management in software engineering. IEEE Softw. 19(3), 26–38 (2002)CrossRefGoogle Scholar
  18. 18.
    Ju, T.L.: Representing organizational memory for computer-aided utilization. J. Inf. Sci. 32(5), 420–433 (2006)CrossRefGoogle Scholar
  19. 19.
    Maedche, A., Motik, B., Stojanovic, L., Studer, R.: Volz, R: Ontologies for enterprise knowledge management. IEEE Intell. Syst. 18(2), 26–33 (2003)CrossRefGoogle Scholar
  20. 20.
    Fensel, D.: Ontology-based knowledge management. IEEE Comput. 35(11), 56–59 (2002)CrossRefGoogle Scholar
  21. 21.
    Chang, J., Choi, B., Lee, H.: An organizational memory for facilitating knowledge: an application to e-business architecture. Expert Syst. Appl. 26(2), 203–215 (2004)CrossRefGoogle Scholar
  22. 22.
    Fernández-López, M. Gómez-Pérez, A. Juristo, N.: METHONTOLOGY: from ontological art towards ontological engineering. In: AAAI-97 Spring Symposium Series, 24–26 March 1997, Stanford University, EEUU (1997)Google Scholar
  23. 23.
    Noy, N.F. McGuinness, D.L.: Ontology development 101: A Guide to Creating Your First Ontology (2001)Google Scholar
  24. 24.
    De Nicola, A., Missikoff, M., Navigli, R.: A proposal for a unified process for ontology building: UPON. In: International Conference on Database and Expert Systems Applications, pp. 655–664. Springer, Berlin, Heidelberg (2005)Google Scholar
  25. 25.
    Tempich, C., Pinto, H.S., Sure, Y., Staab, S.: An Argumentation Ontology for DIstributed, Loosely-controlled and evolvInG Engineering processes of oNTologies (DILIGENT). In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 241–256. Springer, Heidelberg (2005). Scholar
  26. 26.
    Navigli, R., Velardi, P., Cucchiarelli, A., Neri, F. Cucchiarelli, R.: Extending and enriching WordNet with OntoLearn. In: Proceeding of 2nd Global WordNet Conf. (GWC), pp. 279–284 (2004)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.J. Mack Robinson College of BusinessGeorgia State UniversityAtlantaUSA
  2. 2.Center for Data Science and Big Data Analytics, Department of Decision, and Information Sciences, Oakland UniversityRochesterUSA

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