mlsanityData to 14 Oct 2026

EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System

arXiv 2304.04893 ↗First posted 10 Apr 2023cs.AIcited by 6

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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

  1. 2018SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuatorscited by 27
  2. 2021Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directionscited by 24
  3. 2020SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Liftingcited by 23
  4. 2022Towards General-Purpose Representation Learning of Polygonal Geometriescited by 22
  5. 2021Narrative Cartography with Knowledge Graphscited by 3

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Citing papers per year: 2023: 4; 2024: 1; 2025: 1.

6 papers.