Towards a Scalable Architecture for Legal Ontologies Integrated into Digital Twins of Administrative Law

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Abstract

Administrative-law provisions are still published almost exclusively in natural language, forcing every stakeholder to translate identical rules into bespoke code bases—a practice that invites inconsistency, hampers transparency, and inflates maintenance costs. Recent work on Digital Twins for Administrative Law (DTAL) suggests that legislation be issued together with machine-readable ontologies and executable logic, yet guidance on how to architect such systems remains scarce. In this work we propose a layered reference architecture that separates (i) the natural-language statute, (ii) a core ontology expressed in OWL, (iii) a configuration layer for mutable policy parameters, and (iv) an executable-rule layer exposed through a RESTful and MCP façade. Grounded in design science research, we implemented a proof-of-concept twin of the Upper-Austrian tourism-levy statute and qualitatively evaluate the twin with legal, software, and public-administration experts. Early results suggest that ontology-driven twins can reduce duplicate implementations, streamline updates, and enhance legal certainty, thereby strengthening the Rule of Law in automated decision-making.

Keywords: Legal Ontologies, Semantic Interoperability, Automated Decision-Making, Digital Twins
Original languageEnglish
Title of host publicationJoint Proceedings of the 21st International Conference on Semantic Systems (SEMANTiCS 2025), Vienna, Austria, September 03-05, 2025, Joint Proceedings of Posters, Demos, Workshops, and Tutorials
EditorsDavid Chaves-Fraga, Ivan Heibi, Daniel Garijo, Diego Collarana, Angelo A. Salatino, Sahar Vahdati
Number of pages6
Volume4064
ISBN (Electronic)1613-0073
Publication statusPublished - Sept 2025

Publication series

NameCEUR Workshop Proceedings (CEUR-WS.org)

Fields of science

  • 102030 Semantic technologies
  • 502050 Business informatics
  • 102010 Database systems
  • 102035 Data science
  • 503008 E-learning
  • 502058 Digital transformation
  • 509026 Digitalisation research
  • 102033 Data mining
  • 102 Computer Sciences
  • 102027 Web engineering
  • 102028 Knowledge engineering
  • 102016 IT security
  • 102015 Information systems
  • 102025 Distributed systems

JKU Focus areas

  • Digital Transformation

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