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.

Languages, namely, fragments, of the Datalog+/- family are attracting interest in both academia and industry because of their possibility to balance high expressive power and computational complexity. However, understanding the differences among the fragments, mastering them to achieve scalable industrial applications, and communicating their peculiarities to a non-expert audience is challenging for researchers, developers, logicians, and educators. In this demo, we introduce Vadacode, an IDE for Datalog+/- designed to support a broad category of users. The tool offers advanced features, including fragment detection, syntax highlighting, code completion, error diagnostics, schema inference, debugging support, and AI-assisted coding capabilities. Thanks to our experience in the financial context, our demo will guide the audience in modeling financial Datalog+/- programs, showcasing a seamless and effective coding experience.

The Company Control Problem consists in understanding who exerts decision power in companies. Central banks, financial intelligence units, and market regulators are all interested in this problem, which is crucial for their core goals. In the context where these actors operate, changes in company control call for immediate reactions.Yet, computing control relationships is a computationally expensive problem that involves traversing the entire shareholding structure and aggregating shares over multiple paths.In the context of the joint European banking supervision, the Bank of Italy will soon handle the shareholding graph of all European companies, which comprises hundreds of millions of entities (firms and individuals) and billions of edges and properties. This graph is highly volatile as the Bank continuously receives updates about shareholding relationships with unpredictable high frequency. This makes the straightforward bulk solution, where all the company control relationships are computed and materialized whenever a change occurs, unaffordable in practice.In this work, we present an incremental rule-based formalization of the problem, adopting the Vadalog fragment of the Datalog+/- families of languages. Our approach analyzes the specific change, singles out the portions of the graph that are affected by it, and selectively updates them. This allows one both to timely evaluate the impact of ownership variations on an extensive European-scale shareholding graph and to enable economists to perform the so-called "what-if analysis", i.e., simulation scenarios to proactively study the consequences of potential share acquisition operations, that currently are prohibitively time expensive. We provide an extensive experimental evaluation on very large company graphs, comparatively confirming the scalability of our technique in a real production setting.

Logic-based Knowledge Graphs (KGs) are gaining momentum in academia and industry thanks to the rise of expressive and efficient languages for Knowledge Representation and Reasoning (KRR). These languages accurately express business rules, through which valuable new knowledge is derived. A versatile and scalable backend reasoner, like Vadalog, a state-of-the-art system for logic-based KGs---based on an extension of Datalog---executes the reasoning. In this demo, we present KG-Roar, a web-based interactive development and navigation environment for logical KGs. The system lets the user augment an input graph database with intensional definitions of new nodes and edges and turn it into a KG, via the metaphor of reasoning widgets---user-defined or off-the-shelf code snippets that capture business definitions in the Vadalog language. Then, the user can seamlessly browse the original and the derived nodes and edges within a "Virtual Knowledge Graph", which is reasoned upon and generated interactively at runtime, thanks to the scalability and responsiveness of Vadalog. KG-Roar is domain-independent but domain aware, as exploration controls are contextually generated based on the intensional definitions. We walk the audience through KG-Roar showcasing the construction of certain business definitions and putting it into action on a real-world financial KG, from our work with the Bank of Italy.

During the on-going COVID-19 pandemic, online social media have been extensively used by policy makers and health authorities to quickly disseminate useful information and respond to public concerns in a timely fashion. Notwithstanding the huge amount of literature on analyzing positive and negative emotions conveyed by social media users, researchers have not widely investigated the main determinants of online sentiment during crises. To fill this gap, in this paper we analyse a large-scale dataset of over 1.7 M tweets in order to understand whether online feelings, expressed by Italian individuals on Twitter during the pandemic, have been affected by socio-economic and epidemiological variables. Leveraging both panel models and cross-section regressions at different geographical levels, we find that more pessimistic feelings are communicated by users located in areas where the virus hit more severely, with a higher mortality rate and a larger fraction of infected individuals with respect to the local population. Finally, we show that administrative units exhibiting the most positive emotions are those characterized by lower income per capita and larger socio-economic deprivation, suggesting that sentiments in online conversations could be driven by epidemiological factors and by the fear of economic backlashes in wealthier areas of Italy.