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.

Today's large knowledge graphs are conceived mainly for supporting search and e-commerce within large companies such as Google or Amazon, with well-crafted knowledge creation rules. Our recent experience of the COVID-19 pandemic, when knowledge has grown at unprecedented rates and has been often contradictory, inspired us to capture a huge gap in existing concepts and technology: today's knowledge management does not adequately support such a disruptive process. In this article, we propose the design and prototyping of the next generation of knowledge management concepts and systems, which will support domain diversity and scientific evolution as foundational ingredients. Change management is based on a reactive approach, well-established in database systems, but so far lacking in knowledge systems. We propose the reactive interaction of several knowledge hubs, each developed within a scientific domain and “owner” of a portion of a common knowledge representation. Knowledge is represented as graphs, with nodes and edges; edges may inter-connect nodes from different hubs. Most importantly, reactive rules cross the hub's borders and create the premises for a disciplined knowledge evolution, even under the pressure of crises. Similar challenges are not restricted to the recent pandemic and can address other crisis scenarios, including the catastrophic consequences of climate change or the recent (r)-evolution in artificial intelligence, studied by several scientific communities, whose management requires complex and controversial choices.

The human genome complexity is captured by many signals, representing for instance DNA variations, the expression of gene activity, or DNA’s structural rearrangements; a rich set of data types and formats is used to record these signals. Conceptual models can support the description and explanation of the genome’s elaborate structure and behavior. Among others, the Conceptual Schema of the Human Genome (CSG) provides a concept-oriented, top-down representation of the genome behavior, which is independent of data formats. The Genomic Conceptual Model (GCM) provides instead a data-oriented, bottom-up representation, targeting a well-organized, unified description of these formats. In this research, we join the two approaches to achieve PoliViews, a comprehensive model that links (1) a concepts layer, describing genome elements and their conceptual connections, with (2) a data layer, describing datasets derived from genome sequencing with specific technologies. Their dynamic connection is established when specific genomic data types are chosen in the data layer, thereby triggering the selection of a view in the concepts layer. The benefit is mutual: data records can be semantically described by high-level concepts exploiting their links and, in turn, the continuously evolving abstract model can be extended thanks to the input provided by real datasets. PoliViews enables expressing queries that employ a holistic conceptual perspective on the genome, directly translated onto data-oriented terms and organization. Here, we demonstrate the approach by linking two major genomic data types, namely DNA variation and gene expression. For each type, we consider different eminent data sources; we describe their mapping with the corresponding view in the concepts layer, enabling an intra-data-type integration. Then, leveraging on the connections available in the concepts layer, we show how the distinct data types can be interoperated, enabling an inter-data-type integration. The PoliViews approach is shown through several examples of biological interest and can be further extended to any kind of genomic information.

Although the knowledge about human genomics is available to all scientists, information about this scientific breakthrough can often be difficult to fully comprehend and share. A Conceptual Schema of the Human Genome was previously developed to assist in describing human genome-related knowledge, by representing a holistic view of the relevant concepts regarding its biology and underlying mechanisms. This model should become helpful for any researcher who works with human genomics data. We, therefore, perform the process of ontological unpacking on a portion of the model, to facilitate domain understanding and data exchange among heterogeneous systems. The ontological unpacking is a transformation of an input conceptual model into an enriched model based on a foundational ontology. The preliminary analysis and enrichment process are supported by the ontological conceptual modeling language OntoUML, which has been applied previously to complex models to gain ontological clarity. The value of the used method is first assessed from a theoretical point of view: the transformation results in significant, diverse modeling implications regarding the characterization of biological entities, the representation of their changes over time, and, more specifically, the description of chemical compounds. Since the ontological unpacking process is costly, an empirical evaluation is conducted to study the practical implications of applying it in a real learning setting. A particularly complex domain such as metabolic pathways is either described by adopting a traditional conceptual model or explained through an ontologically unpacked model obtained from a traditional model. Our research is evidence that including a strong ontological foundation in traditional conceptual models is useful. It contributes to designing models that convey biological domains better than the original models.