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Legal Definition Annotation in EU Legislation Using Symbolic AI

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Electronic Government and the Information Systems Perspective (EGOVIS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14913))

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Abstract

The definition is an integral component of the legislation. It is essential to the adequacy and legitimacy of legislation. In legislation, legal definitions are used for clarity, consistency, and legitimate certainty, but also for creating new legal concepts (e.g., personal data) or crimes (e.g. stalking, mobbing). With the advancement in society with technological innovation, the significance of accurate and precise definitions in legislation is indeed more articulated. In order to avoid ambiguity and to ensure, as far as possible, a strict interpretation of Law, Legal Texts (LT) usually define the specific lexical terms used within their discourse by means of normative rules. Due to the continuous increase of LT and a large number of domain-specific rules, extracting these definitions from the LT would be costly and time-consuming if it’s done humanely. Definition extraction is widely used in Legal domains to perform legal and compliance analysis. In this paper, we detect and annotate the legal definitions using Symbolic Artificial Intelligence (AI) based on Natural Language Processing (NLP) and fostering LegalXML annotation. The goal is to qualify a very valuable part of legislation for supporting further AI applications also in the judiciary domain. The detection and annotation of definitions are performed on the delimiting type of definitions. The dataset consists of EU Legislation in the span of time from 2010 to 2021 in Akoma Ntoso (AKN) file format. The resultant 15082 AKN files are annotated. (Akoma Ntoso OASIS LegalDocML XML Standard. http://docs.oasis-open.org/legaldocml/akn-core/v1.0/akn-core-v1.0-part1-vocabulary.html)

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Acknowledgments

This study is funded by PON grants of the Italian Government and also the ERC HyperModeLex.

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Authors and Affiliations

  1. CIRSFID-ALMA-AI, University of Bologna, Bologna, Italy

    Muhammad Asif & Monica Palmirani

  2. Department of Computer Science, University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Muhammad Asif

Authors
  1. Muhammad Asif
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  2. Monica Palmirani
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Corresponding author

Correspondence to Monica Palmirani .

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Editors and Affiliations

  1. Corvinus University of Budapest, Budapest, Hungary

    Andrea Kö

  2. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  3. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  4. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

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Asif, M., Palmirani, M. (2024). Legal Definition Annotation in EU Legislation Using Symbolic AI. In: Kö, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2024. Lecture Notes in Computer Science, vol 14913. Springer, Cham. https://doi.org/10.1007/978-3-031-68211-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-68211-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-68210-0

  • Online ISBN: 978-3-031-68211-7

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