Abstract
Over the past decade, the electric vehicle (EV) 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, EV 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.
| Original language | English |
|---|---|
| Pages (from-to) | 949-974 |
| Number of pages | 26 |
| Journal | Transactions in GIS |
| Volume | 27 |
| Issue number | 4 |
| Early online date | 23 May 2023 |
| DOIs | |
| Publication status | Published - 2 Jun 2023 |
Bibliographical note
Funding Information:We would like to thank Professor Krzysztof janowicz for his suggestions on EVKG ontology design. This work is mainly funded by University of Georgia and University of California Davis.
Publisher Copyright:
© 2023 John Wiley & Sons Ltd.
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