EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System
Yanlin Qi, Gengchen Mai, Rui Zhu, and Michael Zhang

TL;DR
The paper introduces EVKG, a comprehensive, interoperable knowledge graph for electric vehicles that integrates multiple data sources to support decision-making in EV technology, infrastructure, and policy development.
Contribution
It presents a novel, extensible EV-centric knowledge graph that integrates existing ontologies and enables interoperability within the Linked Data Open Cloud.
Findings
Demonstrates EVKG's ability to answer diverse EV-related questions
Shows EVKG's integration with other knowledge graphs enhances data richness
Validates EVKG's usefulness for decision-making in EV ecosystem
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Data Quality and Management · Advanced Graph Neural Networks
MethodsOntology
