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How to Understand Better “Smart Vehicle”? Knowledge Extraction for the Automotive Sector Using Web of Things

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Semantic IoT: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 941))

Abstract

How to understand better the knowledge provided by Google results to build future “smart vehicle-centric” applications? What is the knowledge expertise required to build a smart vehicle application (e.g., driver assistance system)? Automotive companies (e.g., Toyota, BMW, Renault) are employing Internet of Things (IoT) and Semantic Web technologies to model the automotive sector. We aggregate this “common sense knowledge” in a automotive dataset which comprises 42 semantics-based projects between 2005 and 2019. The knowledge is already encoded with knowledge representation languages (e.g., RDF, RDFS, and OWL) and supported by the World Wide Web Consortium (W3C). However, only a subset of those projects share their expertise by publishing their ontologies online. For this reason, at the current time or writing, only 16 ontologies are processable. Our innovative Knowledge Extraction for the Automotive Sector (KEAS) methodology analyzes what are the most popular terms required to build a smart car, it provides: (1) a set of keyphrase that are synonyms to smart cars to find domain-specific knowledge, (2) synonyms are used to build a corpus of scientific publications to train the k-means machine learning algorithm, (3) a dataset of smart car ontologies that we collected, is analyzed by the k-means algorithm, and (4) the extraction of the most common terms from the ontology dataset for the automotive sector. Our KEAS findings can be used as a starting point for further domain-specific investigations (e.g., Volvo willing to integrate semantic web) and for future information extraction from structured knowledge.

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Notes

  1. 1.

    http://bit.ly/2xMZQDv.

  2. 2.

    https://gtnr.it/2SgUvOi.

  3. 3.

    http://bit.ly/2Y3A1xL.

  4. 4.

    http://www.bmwsummerschool.com/.

  5. 5.

    http://automotive.eurecom.fr/vsso.

  6. 6.

    http://automotive.eurecom.fr/vdc.

  7. 7.

    https://auto.schema.org/.

  8. 8.

    https://twitter.com/olafhartig/status/1121539105924550661.

  9. 9.

    http://lov4iot.appspot.com/?p=lov4iot-transport.

  10. 10.

    “Smart car ontology” search on Google, December 2018.

  11. 11.

    http://bit.ly/2Y3A1xL.

  12. 12.

    https://auto.schema.org/.

  13. 13.

    http://ci.emse.fr/opensensingcity/ns/result/domain/transportation/.

  14. 14.

    http://shorturl.at/jEIQ7.

  15. 15.

    http://lov4iot.appspot.com/?p=lov4iot-transport.

  16. 16.

    https://www.w3.org/TR/wot-thing-description/.

  17. 17.

    https://bildungsportal.sachsen.de/survey/limesurvey/index.php/716626/lang-en.

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Acknowledgements

This work has partially received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 857237 (Interconnect). The opinions expressed are those of the authors and do not reflect those of the sponsors.

Author information

Authors and Affiliations

  1. Technische Universitat Chemnitz, Chemnitz, Germany

    Mahda Noura & Martin Gaedke

  2. Kno.e.sis, Wright State University, Dayton, USA

    Amélie Gyrard

  3. EURECOM, Sophia Antipolis, Biot, France

    Benjamin Klotz, Raphael Troncy & Soumya Kanti Datta

  4. Trialog, Paris, France

    Amélie Gyrard

Authors
  1. Mahda Noura
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  2. Amélie Gyrard
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  3. Benjamin Klotz
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  4. Raphael Troncy
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  5. Soumya Kanti Datta
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  6. Martin Gaedke
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Corresponding author

Correspondence to Amélie Gyrard .

Editor information

Editors and Affiliations

  1. Amity Institute of Information Technology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India

    Rajiv Pandey

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Marcin Paprzycki

  3. Amity Institute of Information Technology, Amity University Uttar Pradesh Lucknow Campus, Lucknow, Uttar Pradesh, India

    Nidhi Srivastava

  4. Database System Laboratory, University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Subhash Bhalla

  5. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Katarzyna Wasielewska-Michniewska

7 Appendix

7 Appendix

1.1 7.1 Clustering Results

See Figs. 3, 4, 5, 6, 7, 8 and 9

Fig. 3
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Cluster Results Part I

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Cluster Results Part II

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Cluster Results Part III

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Cluster Results Part IV

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Cluster Results Part V

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Fig. 8
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Cluster Results Part VI labelfig

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Fig. 9
figure 9

Cluster Results Part VII

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Noura, M., Gyrard, A., Klotz, B., Troncy, R., Datta, S.K., Gaedke, M. (2021). How to Understand Better “Smart Vehicle”? Knowledge Extraction for the Automotive Sector Using Web of Things. In: Pandey, R., Paprzycki, M., Srivastava, N., Bhalla, S., Wasielewska-Michniewska, K. (eds) Semantic IoT: Theory and Applications. Studies in Computational Intelligence, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-64619-6_13

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