EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System
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Abstract (arXiv)
Over the past decade, the electric vehicle industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, electric vehicle supply equipment, and electricity transmission network, to support decision-making related to EV technology development, infrastructure planning, and policy-making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV-relevant ontology modules from existing well-known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV-related knowledge into a single, easily accessible resource, the EVKG supports decision-makers in making informed choices about EV technology development, infrastructure planning, and policy-making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 5 of its 74 references that are in the map. First posted on arXiv 10 Apr 2023.
Builds on
5 of 74 references.
References · 5
- 2018SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuatorscited by 27
- 2021Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directionscited by 24
- 2020SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Liftingcited by 23
- 2022Towards General-Purpose Representation Learning of Polygonal Geometriescited by 22
- 2021Narrative Cartography with Knowledge Graphscited by 3
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Citing papers per year: 2023: 4; 2024: 1; 2025: 1.
6 papers.
- 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligencecited by 88
- 2023Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applicationscited by 21
- 2023Multimodality of AI for Education: Towards Artificial General Intelligencecited by 21
- 2023AGI: Artificial General Intelligence for Educationcited by 7
- 2025RecKG: Knowledge Graph for Recommender Systemscited by 4
- 2024The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphscited by 1
- 2025RecKG: Knowledge Graph for Recommender Systems7 Jan 2025
- 2024The S2 Hierarchical Discrete Global Grid as a Nexus for Data Representation, Integration, and Querying Across Geospatial Knowledge Graphs18 Oct 2024
- 2023Multimodality of AI for Education: Towards Artificial General Intelligence10 Dec 2023
- 2023Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications20 Jun 2023
- 2023AGI: Artificial General Intelligence for Education24 Apr 2023
- 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence13 Apr 2023