Linked Data allows the description of domain-level knowledge that is understandable by both humans and machines. Nevertheless, as machines are intolerant of unexpected input, the quality of the underlying Linked Data largely determines the success of the envisaged Semantic Web. Despite, though, the significant number of existing tools, generating Linked Data by incorporating heterogeneous data from multiple sources and different formats into the Linked Open Data cloud remained complicated, let alone generating their metadata. Raw data values are expected to be used as extracted, while when data transformations occur, they remain coupled and case-specific in separate not-reusable systems. Moreover, quality assessment is performed after Linked Data is published and adjustments are manually – but rarely – applied, while the violations root is not identified. In this talk, we present a sustainable semantic-driven approach, based on the RML toolchain (RML Mapper, RML Workbench, RML Editor and RML Validator), which we adopt to address the aforementioned shortcomings and enables data owners to generate high quality Linked Data by themselves. This way, we facilitate and automate the generation of high quality Linked Data with accurate, consistent and complete metadata, offering a granular, sustainable and generic solution that shortens the Linked Data generation workflow, and achieves higher integrity within Linked Data.