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)


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.


Ontology learning Ontology matching Deep learning 



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


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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

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