Deductive rules over graph data are a commonly accepted way to address complex reasoning tasks; among them, we mention the company control problem, which consists of determining who exercises control - directly or indirectly, through aggregation and recursion - over ownership graphs. Solving this and similar problems is crucial for the Central Bank of Italy; the Bank uses Vadalog, a state-of-the-art proprietary reasoner based on an extended Datalog, to routinely manage changes (insertions and deletions) of ownership in large graphs covering all Italian companies. However, at a smaller scale, similar activities are also relevant in more targeted activities, e.g., for financial intelligence tasks in the public and private sectors. In this paper, we present a general scheme for generating active rules that correctly handle recursion, aggregation, and stratified negation, so as to deploy reactive reasoners over graph data managers. We show how to convert high-level reasoning rules expressed in Datalog into triggers as Cypher statements, the most aligned language with the recently standardized Graph Query Language. We discuss how Cypher triggers can be managed by a dedicated controller that replicates the reasoning capabilities of a deductive reasoner engine within a graph database system. We implement the controller within Neo4j, the most widespread open-source graph database, demonstrating that our implementation achieves adequate performance over small-to-medium property graphs. We also show that our approach is general and applicable to other domains (e.g., laws), directly allowing reasoning with deductive rules over graph databases. Finally, we discuss how the translation process from Datalog to Cypher can be facilitated by state-of-the-art pre-trained Large Language Models, capable of accurately performing the translation task.

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. • A Property Graph is used to model the complexities of legislative systems. • A replicable ETL pipeline generates a Knowledge Graph of the Italian legislation. • Large Language Models assist and support in achieving a higher quality. • The graph database enables powerful analysis of the legislative system.

Knowledge Graphs (KGs) have been used to organize large datasets into structured, interconnected information, enhancing data analytics across various fields. In the legislative context, one potential natural application of KGs is modeling the intricate set of interconnections that link laws and their articles with each other and the broader legislative context.

Linking entities from different datasets is a crucial task for the success of modern businesses. However, aligning entities becomes challenging as common identifiers might be missing. Therefore, the process should rely on string-based attributes, such as names or addresses, thus harming precision in the matching. At the same time, powerful general-purpose record linkage tools require users to clean and pre-process the initial data, introducing a bottleneck in the success of the data integration activity and a burden on actual users. Furthermore, scalability has become a relevant issue in modern big data environments, where a lot of data flows daily from external sources. This work presents a novel record linkage tool, BeRTo, that addresses the problem of linking a specific type of data source, i.e., business registries, containing information about companies and corporations. While being domain-specific harms its usability in other contexts, it manages to reach a new frontier in terms of precision but also scalability, as it has been built on Spark. Integrating the pre-processing and cleaning steps in the same tool creates a user-friendly end-to-end pipeline that requires users only to input the raw data and set their preferred configuration, allowing to focus on recall or precision.