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.
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Acknowledgements
The work providing these results has received funding with Dora Plus Action scholarship from Tallinn University of Technology in Estonia.
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Nogales, A., Täks, E., Taveter, K. (2019). Ontology Modeling of the Estonian Traffic Act for Self-driving Buses. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_24
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DOI: https://doi.org/10.1007/978-3-030-11680-4_24
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