Legal Knowledge Graph Foundations, Part I: URI-Addressable Abstract Works (LRMoo F1 to schema.org)
May 12, 2025 Β· Declared Dead Β· + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hudson de Martim
arXiv ID
2508.00827
Category
cs.DL: Digital Libraries
Cross-listed
cs.AI,
cs.CY,
cs.IR
Citations
0
Last Checked
3 months ago
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Digital Libraries
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Measuring academic influence: Not all citations are equal
R.I.P.
π»
Ghosted
The Open Access Advantage Considering Citation, Article Usage and Social Media Attention
R.I.P.
π»
Ghosted
A Bibliometric Review of Large Language Models Research from 2017 to 2023
R.I.P.
π»
Ghosted
On the Performance of Hybrid Search Strategies for Systematic Literature Reviews in Software Engineering
R.I.P.
π»
Ghosted
A Systematic Identification and Analysis of Scientists on Twitter
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted