An LLM-assisted ETL pipeline to build a high-quality knowledge graph of the Italian legislation

Abstract

The increasing complexity of legislative systems, characterized by an ever-growing number of laws and their interdependencies, has highlighted the utility of Knowledge Graphs (KGs) as an effective data model for organizing such information, compared to traditional methods, often based on relational models, which struggle to efficiently represent interlinked data, such as references within laws, hindering efficient knowledge discovery.

A paradigm shift in modeling legislative data is already ongoing with the adoption of common international standards, predominantly XML-based, such as Akoma Ntoso (AKN) and the Legal Knowledge Interchange Format, which aim to capture fundamental aspects of laws shared across different legislations and simplify the task of creating Knowledge Graphs through the use of XML tags and identifiers. However, to enable advanced analysis and data discovery within these KGs, it is necessary to carefully check, complement, and enrich KG nodes and edges with properties, either metadata or additional derived knowledge, that enhance the quality and utility of the model, for instance, by leveraging the capabilities of state-of-the-art Large Language Models.

In this paper, we present an ETL pipeline for modeling and querying the Italian legislation in a Knowledge Graph, by adopting the property graph model and the AKN standard implemented in the Italian system. The property graph model offers a good compromise between knowledge representation and the possibility of performing graph analytics, which we consider essential for enabling advanced pattern detection. Then, we enhance the KG with valuable properties by employing carefully fine-tuned open-source LLMs, i.e., BERT and Mistral-7B models, which enrich and augment the quality of the KG, allowing in-depth analysis of legislative data.