Structuring legal norms for machine readability is a critical prerequisite for building advanced AI and information retrieval systems, such as Legal Knowledge Graphs (LKGs). Grounded in the Functional Requirements for Bibliographic Records (FRBR) model, this paper proposes a foundational mapping for the abstract legal Work—which is materialized as the Norm node in our legal Graph RAG framework—to the interoperable schema.org/Legislation vocabulary. Using the Normas.leg.br portal as a practical case study, we demonstrate how to describe this Work entity via JSON-LD, considering stable URN identifiers, inter-norm relationships, and lifecycle properties. This structured, formal approach provides the essential first step toward creating a deterministic and verifiable knowledge graph, which can serve as a formalized "ground truth" for Legal AI applications, overcoming the limitations of purely probabilistic models.
Hudson de Martim (Mon,) studied this question.