Advertisement

Ontology Modeling of the Estonian Traffic Act for Self-driving Buses

  • Alberto NogalesEmail author
  • Ermo Täks
  • Kuldar Taveter
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 898)

Abstract

The development of self-driving cars is a major research area that has led to several still unresolved issues. One of them is the need to abide by the legal stipulations fixed by a traffic act concerning the territory of operation. An appropriate solution to make text understandable by machines is the use of ontologies. This paper presents a first approach where the Estonian Traffic Act is transformed from text into populated ontologies, so it can be understood by machines. The proposal is a (semi)-automatic ontology learning process that combines natural language processing (NLP) and ontology matching techniques with a deep learning model. The results show that 78% of the norms that have been considered valid can be modelled with the method described in the paper.

Keywords

Ontology learning Ontology matching Deep learning 

Notes

Acknowledgements

The work providing these results has received funding with Dora Plus Action scholarship from Tallinn University of Technology in Estonia.

References

  1. 1.
    Blyth, P., Mladenović, M., Nardi, B., Su, N., Ekbia, H.: Driving the self-driving vehicle: expanding the technological design horizon. In: Proceedings of the International Symposium on Technology and Society (ISTAS), pp. 1–6 (2015)Google Scholar
  2. 2.
    Gruber, T.: A translation approach to portable ontologies. Knowl. Acquis. 5(2), 199–220 (1993)CrossRefGoogle Scholar
  3. 3.
    Maedche, A., Staab, S.: Ontology learning for the semantic web. IEEE Intell. Syst. 16(2), 72–79 (2001)CrossRefGoogle Scholar
  4. 4.
    Fernandez, S., Ito, T., Hadfi, R.: Architecture for intelligent transportation system based in a general traffic ontology. In: Lee, R. (ed.) Computer and Information Science 2015. SCI, vol. 614, pp. 43–55. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-23467-0_4CrossRefGoogle Scholar
  5. 5.
    Zhao, L., Ichise, R., Mita, S., Sasaki, Y.: Ontologies for advanced driver assistance systems. J. Jpn. Soc. Artif. Intell. (2015)Google Scholar
  6. 6.
    Armand, A., Filliat, D., Guzman, J.I.: Ontology-based context awareness for driving assistance systems. In: IEEE Intelligent Vehicles Symposium Proceedings, pp. 227–233 (2014)Google Scholar
  7. 7.
    Jiang, X., Tan, A.: CRCTOL: a semantic-based domain ontology learning system. J. Am. Soc. Inform. Sci. Technol. 61(1), 150–168 (2009)CrossRefGoogle Scholar
  8. 8.
    Tang, J., Leung, H., Luo, Q., Chen, D., Gong, J.: Towards ontology learning from folksonomies. In: Proceedings of the 21st International Conference on Artificial Intelligence (JCAI 2009), pp. 2089–2094 (2009)Google Scholar
  9. 9.
    Völker, J., Fernandez Langa, S., Sure, Y.: Supporting the construction of Spanish legal ontologies with Text2Onto. In: Casanovas, P., Sartor, G., Casellas, N., Rubino, R. (eds.) Computable Models of the Law. LNCS (LNAI), vol. 4884, pp. 105–112. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-85569-9_7CrossRefGoogle Scholar
  10. 10.
    Buitelaar, P., Cimiano, P., Magnini, B.: Ontology learning from text: an overview. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Frontiers in Artificial Intelligence and Applications, Ontology Learning from Text: Methods, Evaluation and Applications, vol. 123, pp. 3–12 (2005)Google Scholar
  11. 11.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)CrossRefGoogle Scholar
  12. 12.
    Kilgarriff, A., Fellbaum, C.: WordNet: an electronic lexical database. Lang. speak Commun. 76(3), 706 (2000)Google Scholar
  13. 13.
    Linked Open Vocabularies. https://lov.linkeddata.es/dataset/lov/
  14. 14.
    Chiarcos, C., Sukhareva, M.: OLiA – ontologies of linguistic annotation. Semant. Web 6(4), 379–386 (2015)CrossRefGoogle Scholar
  15. 15.
    Bengio, Y., Courville, A.C., Goodfellow, I.J., Hinton, G.E.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  16. 16.
    English Traffic Estonian Act in XML. https://www.riigiteataja.ee/en/tolge/xml/507012014005
  17. 17.
    NLTK Python package. https://www.nltk.org/
  18. 18.
    Fung, S.Y.C., Watson-Brown, A.: The Template: A Guide for the Analysis of Complex Legislation. Institute of Advanced Legal Studies Location, London (1994)Google Scholar
  19. 19.
    Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL-03), pp. 423–430 (2003)Google Scholar
  20. 20.
    Santorini, B.: Part-of-speech tagging guidelines for the Penn Treebank Project. Department of Computer and Information Science, University of Pennsylvania (1990)Google Scholar
  21. 21.
    RDFlLib Python package. https://rdflib.readthedocs.io/
  22. 22.
    Baumfalk, J., Dastani, M., Poot, B., Testerink, B.: A SUMO extension for norm-based traffic control systems. In: Behrisch, M., Weber, M. (eds.) Simulating Urban Traffic Scenarios. LNM, pp. 55–82. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-33616-9_5CrossRefGoogle Scholar
  23. 23.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)Google Scholar
  24. 24.
    Keras Python package. https://keras.io/
  25. 25.
    Laufer, B., Sim, D.D.: Taking the easy way out: non-use and misuse of contextual clues in EFL reading comprehension. Engl. Teach. Forum 23, 7–10 (1985)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CEIEC, Research Institute, Universidad Francisco de Vitoria (UFV)Pozuelo de AlarcónSpain
  2. 2.Department of InformaticsTallinn University of TechnologyTallinnEstonia

Personalised recommendations