The rapid proliferation of environmental sensor networks (ESNs) used for monitoring environmental systems, such as meteorology and air quality, and advances in database technologies [e.g., structured query language (SQL)] has made significant progress in sensor data management. Notwithstanding the strength of these databases, they can inevitably lead to a data heterogeneity problem as a result of isolated databases with distinct data schema, which are expensive to be accessed and preprocessed when the data are consumed spanning multiple databases. Recently, knowledge graphs have been used as one of the most popular integration frameworks to address this data heterogeneity problem from the perspective of establishing an interoperable semantic schema (also known as ontology). However, the majority of the proposed knowledge graphs in this domain are a product of an extraction–transform–load approach with all the data physically stored in a triplestore. In contrast, this article examines an approach of virtualizing knowledge graphs on top of the SQL databases as the means to provide a federated data integration approach for enhanced heterogeneous ESNs' data access, bringing with it the promise of more cost efficiency in terms of input/output, storage, etc. In addition, this work also considers some motivating application scenarios regarding the efficiency of time-series data access. Based on a performance comparison between the proposed integration approach and some popular triplestores, the proposed approach has a significant edge over triplestores in multiple time-series structuring and acquisition.