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Legal Knowledge Graph Foundations, Part I: URI-Addressable Abstract Works (LRMoo F1 to schema.org)

Hudson de Martim

TL;DR

This paper addresses making legal norms machine-readable on the web by grounding a formal, event-centric representation of normative content in Schema.org/Legislation to publish the abstract Work (F1) as a URI-addressable resource. It details a property-by-property mapping of the F1 Work to sdo:Legislation, validated with a Brazilian case study from Normas.leg.br and a JSON-LD example that anchors the norm with stable identifiers, core metadata, and inter-norm relationships. This work provides a verifiable 'ground truth' foundation for subsequent Expressions and Components, enabling deterministic, temporally-aware Legal Knowledge Graphs and more reliable AI-assisted retrieval. By situating Schema.org as a web-scale discovery layer compatible with LRMoo, it sets the stage for Part II (Expressions) and future components to complete the lifecycle on the web.

Abstract

Building upon a formal, event-centric model for the diachronic evolution of legal norms grounded in the IFLA Library Reference Model (LRMoo), this paper addresses the essential first step of publishing this model's foundational entity-the abstract legal Work (F1)-on the Semantic Web. We propose a detailed, property-by-property mapping of the LRMoo F1 Work to the widely adopted schema.org/Legislation vocabulary. Using Brazilian federal legislation from the Normas.leg.br portal as a practical case study, we demonstrate how to create interoperable, machine-readable descriptions via JSON-LD, focusing on stable URN identifiers, core metadata, and norm relationships. This structured mapping establishes a stable, URI-addressable anchor for each legal norm, creating a verifiable "ground truth". It provides the essential, interoperable foundation upon which subsequent layers of the model, such as temporal versions (Expressions) and internal components, can be built. By bridging formal ontology with web-native standards, this work paves the way for building deterministic and reliable Legal Knowledge Graphs (LKGs), overcoming the limitations of purely probabilistic models.

Legal Knowledge Graph Foundations, Part I: URI-Addressable Abstract Works (LRMoo F1 to schema.org)

TL;DR

This paper addresses making legal norms machine-readable on the web by grounding a formal, event-centric representation of normative content in Schema.org/Legislation to publish the abstract Work (F1) as a URI-addressable resource. It details a property-by-property mapping of the F1 Work to sdo:Legislation, validated with a Brazilian case study from Normas.leg.br and a JSON-LD example that anchors the norm with stable identifiers, core metadata, and inter-norm relationships. This work provides a verifiable 'ground truth' foundation for subsequent Expressions and Components, enabling deterministic, temporally-aware Legal Knowledge Graphs and more reliable AI-assisted retrieval. By situating Schema.org as a web-scale discovery layer compatible with LRMoo, it sets the stage for Part II (Expressions) and future components to complete the lifecycle on the web.

Abstract

Building upon a formal, event-centric model for the diachronic evolution of legal norms grounded in the IFLA Library Reference Model (LRMoo), this paper addresses the essential first step of publishing this model's foundational entity-the abstract legal Work (F1)-on the Semantic Web. We propose a detailed, property-by-property mapping of the LRMoo F1 Work to the widely adopted schema.org/Legislation vocabulary. Using Brazilian federal legislation from the Normas.leg.br portal as a practical case study, we demonstrate how to create interoperable, machine-readable descriptions via JSON-LD, focusing on stable URN identifiers, core metadata, and norm relationships. This structured mapping establishes a stable, URI-addressable anchor for each legal norm, creating a verifiable "ground truth". It provides the essential, interoperable foundation upon which subsequent layers of the model, such as temporal versions (Expressions) and internal components, can be built. By bridging formal ontology with web-native standards, this work paves the way for building deterministic and reliable Legal Knowledge Graphs (LKGs), overcoming the limitations of purely probabilistic models.

Paper Structure

This paper contains 19 sections, 2 figures.

Table of Contents

  1. Introduction
  2. The Significance of Open Government Data
  3. The Challenge of Structuring Legal Norms for Machine Readability
  4. Our Contribution: A Schema.org Mapping for Brazilian Legal Works
  5. Background and Related Works
  6. Foundational Technologies
  7. The Linked Data Principles
  8. Schema.org Vocabulary: A Bridge for the LRMoo Work Entity
  9. Related Works
  10. Akoma Ntoso (AKN)
  11. LexML Brazil
  12. European Legislation Identifier (ELI)
  13. Mapping the Legal Work to sdo:Legislation
  14. Case Study: Implementation for Brazilian Federal Legislation
  15. An Event-Centric View of the Legal Lifecycle
  16. ...and 4 more sections

Figures (2)

  • Figure 1: Simplified diagram of the Schema.org vocabulary, according to the proposed mapping in this work.
  • Figure 2: LRMoo model of the enactment event (F28 Expression Creation).