Knowledge Graphs (KGs) are published across diverse repositories using heterogeneous metadata models that vary in their descriptive elements, level of detail, vocabularies, and support for semantic interoperability, in part due to the absence of a standardized specification. This lack of consistency in metadata practices hinders effective discovery and reuse of KGs. To address this gap, we present a KG Metadata Specification and Validation Framework designed to foster structured, FAIR-aligned KG descriptions. Developed through a community-driven process, the specification defines 33 metadata elements formalized using the Shapes Constraint Language (SHACL) to enable automated validation. We applied the framework to metadata from 1,573 datasets in the Linked Open Data (LOD) Cloud by mapping its 17 metadata fields to those in our specification. This mapping demonstrated the specification’s compatibility with the largest Linked Data repository and validation process revealed common issues and areas for improvement in metadata quality. Furthermore, we demonstrated how metadata can be published in a way that enhances its visibility for end users and its discoverability by search engines, achieving full compliance with FAIR-Checker evaluations through the publication of specification-conformant metadata for the Food Health Claims KG. The framework provides a foundation for KG publishers, repository managers, and researchers, promoting uniform, high-quality metadata practices and advancing FAIR principles within the KG community.