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Article

Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO)

Vicomtech, Parque Científico y Tecnológico de Gipuzkoa, Mikeletegi Pasealekua, 57, 20009 Donostia-San Sebastian, Spain
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Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(17), 7782; https://doi.org/10.3390/app11177782
Submission received: 3 August 2021 / Revised: 21 August 2021 / Accepted: 22 August 2021 / Published: 24 August 2021
(This article belongs to the Special Issue The Development and Prospects of Autonomous Driving Technology)

Abstract

Modern Artificial Intelligence (AI) methods can produce a large quantity of accurate and richly described data, in domains such as surveillance or automation. As a result, the need to organize data at a large scale in a semantic structure has arisen for long-term data maintenance and consumption. Ontologies and graph databases have gained popularity as mechanisms to satisfy this need. Ontologies provide the means to formally structure descriptive and semantic relations of a domain. Graph databases allow efficient and well-adapted storage, manipulation, and consumption of these linked data resources. However, at present, there is no a universally defined strategy for building AI-oriented ontologies for the automotive sector. One of the key challenges is the lack of a global standardized vocabulary. Most private initiatives and large open datasets for Advanced Driver Assistance Systems (ADASs) and Autonomous Driving (AD) development include their own definitions of terms, with incompatible taxonomies and structures, thus resulting in a well-known lack of interoperability. This paper presents the Automotive Global Ontology (AGO) as a Knowledge Organization System (KOS) using a graph database (Neo4j). Two different use cases for the AGO domain ontology are presented to showcase its capabilities in terms of semantic labeling and scenario-based testing. The ontology and related material have been made public for their subsequent use by the industry and academic communities.
Keywords: semantics; ontology; scenario-based testing; AD; ADAS; labeling; graph database; Neo4j semantics; ontology; scenario-based testing; AD; ADAS; labeling; graph database; Neo4j

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MDPI and ACS Style

Urbieta, I.; Nieto, M.; García, M.; Otaegui, O. Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO). Appl. Sci. 2021, 11, 7782. https://doi.org/10.3390/app11177782

AMA Style

Urbieta I, Nieto M, García M, Otaegui O. Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO). Applied Sciences. 2021; 11(17):7782. https://doi.org/10.3390/app11177782

Chicago/Turabian Style

Urbieta, Itziar, Marcos Nieto, Mikel García, and Oihana Otaegui. 2021. "Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO)" Applied Sciences 11, no. 17: 7782. https://doi.org/10.3390/app11177782

APA Style

Urbieta, I., Nieto, M., García, M., & Otaegui, O. (2021). Design and Implementation of an Ontology for Semantic Labeling and Testing: Automotive Global Ontology (AGO). Applied Sciences, 11(17), 7782. https://doi.org/10.3390/app11177782

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