Abstract
Nowadays, cars are equipped with hundreds of sensors that support a variety of features ranging from basic functionalities to advanced driver assistance systems. The communication protocol of automotive signals is defined in DBC files, yet, signal descriptions are typically ambiguous and vary across manufacturers. In this work, we address the problem of extracting the semantic data from DBC files, which is then managed in an Automotive Signal Knowledge Graph (ASKG). We developed a semi-automatic tool that automatically extracts signals from DBC files and computes candidate links to the ontology. These candidates can then be revised by experts who can also extend the ontology to accommodate new signal types in a pay-as-you-go manner. The knowledge provided by the experts is stored in the ASKG and exploited by the tool thereafter. We conducted an evaluation of the tool based on a targeted experiment with automotive experts and report on the first lessons learned from the usage of the tool in the context of the Bosch automotive data lake. The results show that our solution can correctly populate the ASKG and that the expert effort is reduced over time.
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Vector Informatik GmbH. DBC Communication Database for CAN, https://www.vector.com/.
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Developed semantic artifacts, the detailed setup of the evaluation experiment and its results are available at https://github.com/YuliaS/cannotator.
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From the article: https://en.wikipedia.org/wiki/Automotive_acronyms_and_abbreviations.
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wn, wp, ex stand for the IRIs of WordNet, Wikipedia, and the example source, respectively.
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For hash-URIs/slash-URIs we consider the text after the hash/last slash.
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https://github.com/commaai/opendbc, retrieved on Jul 27, 2020.
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Svetashova, Y., Heling, L., Schmid, S., Acosta, M. (2021). Pay-as-you-go Population of an Automotive Signal Knowledge Graph. In: Verborgh, R., et al. The Semantic Web. ESWC 2021. Lecture Notes in Computer Science(), vol 12731. Springer, Cham. https://doi.org/10.1007/978-3-030-77385-4_43
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