Modelling Legislative Systems into Property Graphs to Enable Advanced Pattern Detection
<|reference_start|>Modelling Legislative Systems into Property Graphs to Enable Advanced Pattern Detection: Legislative systems face growing complexity due to the ever-increasing number of laws and intricate interdependencies between them. Traditional methods of storing and analyzing legal systems, mainly based on RDF, struggle with this complexity, hindering efficient knowledge discovery, as required by domain experts. In this paper, we propose to model legislation into a property graph, where edges represent citations, modifications, and abrogations between laws and their articles or attachments, both represented as nodes and edges with properties. As a practical use case, we implement the model in the Italian legislative system. First, we describe our approach to extracting knowledge from legal texts. To this aim, we leverage the recently internationally adopted XML law standard, Akoma Ntoso, to parse and identify entities, relationships and properties. Next, we describe the model and the schema implemented using Neo4j, the market-leading graph database management system. The schema is designed to capture the structure and hierarchy of laws, together with their interdependencies. We show how such a property graph enables an efficient answer to complex and relevant queries previously impractical on raw text. By leveraging other implementations of the Akoma Ntoso standard and the proposed property graph approach, we are confident that this work will facilitate a comprehensive comparison of legislative systems and their complexities.<|reference_end|>
@article{colombo2024modelling,
    title={Modelling Legislative Systems into Property Graphs to Enable Advanced
  Pattern Detection},
    author={Andrea Colombo, Anna Bernasconi, Stefano Ceri},
    journal={arXiv preprint arXiv:2406.14935},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14935},
    primaryClass={cs.DB}
}