How to Understand Better “Smart Vehicle”? Knowledge Extraction …
311
Fig. 2 Web service example to automatically retrieve ontology-based projects for the smart car
domain: ontology URLs and scientic publications
were in Chinese or German. We released the list of ontology URLs within an online
table.
14 For the convenience of the developers, we created a tutorial web page (http://
lov4iot.appspot.com/?p=queryTransportOntologiesWS) to either use the web service
or easily download the dump of the ontology code that we collected.
For instance, the developer can query the web service http://lov4iot.appspot.com/
perfectoOnto/getOntoDomain/?domain=Transportation which returns the list of the
projects relevant for the smart car domain that we collected within the LOV4IoT
ontology catalog for transport,
15 it includes: the name of the project and the ontology,
the ontology URL, and additional information such as the scientific publication
describing the ontology (see Fig. 2). The web service is more up-to-date with latest
ontologies collected, compared to the dump file. However, when the projects are not
maintained anymore, the URLs can become dead links, which is the reason we store
the ontology code within dump files.
5 Evaluation
Planned Evaluation: To identify the most popular concepts from smart car ontologies, the proposed KEAS methodology is evaluated in an empirical study which
includes an analysis that gives a complete overview of the performance of the descriptiveness of the most popular concepts (in the same way it has been done in our
Knowledge Extraction for the Web o Things (KE4WoT) work [61]). The objective
of this experiment is to identify if the keywords provided by KE4WoT can sufficiently
describe existing ontologies.
14 http://shorturl.at/jEIQ7.
15 http://lov4iot.appspot.com/?p=lov4iot-transport.
311
Fig. 2 Web service example to automatically retrieve ontology-based projects for the smart car
domain: ontology URLs and scientic publications
were in Chinese or German. We released the list of ontology URLs within an online
table.
14 For the convenience of the developers, we created a tutorial web page (http://
lov4iot.appspot.com/?p=queryTransportOntologiesWS) to either use the web service
or easily download the dump of the ontology code that we collected.
For instance, the developer can query the web service http://lov4iot.appspot.com/
perfectoOnto/getOntoDomain/?domain=Transportation which returns the list of the
projects relevant for the smart car domain that we collected within the LOV4IoT
ontology catalog for transport,
15 it includes: the name of the project and the ontology,
the ontology URL, and additional information such as the scientific publication
describing the ontology (see Fig. 2). The web service is more up-to-date with latest
ontologies collected, compared to the dump file. However, when the projects are not
maintained anymore, the URLs can become dead links, which is the reason we store
the ontology code within dump files.
5 Evaluation
Planned Evaluation: To identify the most popular concepts from smart car ontologies, the proposed KEAS methodology is evaluated in an empirical study which
includes an analysis that gives a complete overview of the performance of the descriptiveness of the most popular concepts (in the same way it has been done in our
Knowledge Extraction for the Web o Things (KE4WoT) work [61]). The objective
of this experiment is to identify if the keywords provided by KE4WoT can sufficiently
describe existing ontologies.
14 http://shorturl.at/jEIQ7.
15 http://lov4iot.appspot.com/?p=lov4iot-transport.