How to Understand Better “Smart
Vehicle”? Knowledge Extraction for the
Automotive Sector Using Web of Things
Mahda Noura, Amélie Gyrard, Benjamin Klotz, Raphael Troncy,
Soumya Kanti Datta, and Martin Gaedke
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
M. Noura · M. Gaedke
Technische Universitat Chemnitz, Chemnitz, Germany
e-mail: mahda.noura@informatik.tu-chemnitz.de
M. Gaedke
e-mail: martin.gaedke@informatik.tu-chemnitz.de
A. Gyrard (B)
Kno.e.sis, Wright State University, Dayton, USA
e-mail: amelie@knoesis.org
B. Klotz · R. Troncy · S. K. Datta
EURECOM, Sophia Antipolis, Biot, France
e-mail: benjamin.klotz@eurecom.fr
R. Troncy
e-mail: raphael.troncy@eurecom.fr
S. K. Datta
e-mail: soumya-kanti.datta@eurecom.fr
A. Gyrard
Trialog, Paris, France
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_13
303
Vehicle”? Knowledge Extraction for the
Automotive Sector Using Web of Things
Mahda Noura, Amélie Gyrard, Benjamin Klotz, Raphael Troncy,
Soumya Kanti Datta, and Martin Gaedke
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
M. Noura · M. Gaedke
Technische Universitat Chemnitz, Chemnitz, Germany
e-mail: mahda.noura@informatik.tu-chemnitz.de
M. Gaedke
e-mail: martin.gaedke@informatik.tu-chemnitz.de
A. Gyrard (B)
Kno.e.sis, Wright State University, Dayton, USA
e-mail: amelie@knoesis.org
B. Klotz · R. Troncy · S. K. Datta
EURECOM, Sophia Antipolis, Biot, France
e-mail: benjamin.klotz@eurecom.fr
R. Troncy
e-mail: raphael.troncy@eurecom.fr
S. K. Datta
e-mail: soumya-kanti.datta@eurecom.fr
A. Gyrard
Trialog, Paris, France
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_13
303