Keynote Talk
From Strings to Things and Back: Putting Logic-based Reasoning into Action in a Central Bank
The talk will present a story of the development of new methodologies, tools, and systems in the Datalog+/- realm, conducted through multiple projects with the Joint Knowledge Graph Lab, which includes the Applied Research Team of the Bank of Italy and the KG Lab of TU Wien. Specifically, a concise overview of recent advancements in implementing scalable Datalog+/- fragments in the Vadalog System will be interleaved with real-world applications in the central banking domain. These applications cover areas such as banking supervision, economic research, data privacy, and large-scale reasoning in complex financial Knowledge Graphs.
Given the recent emergence of Large Language Models, the talk will be an opportunity to discuss the evolving role of logic-based reasoning systems based on Knowledge Graphs (“things, not strings”, as Google stated in 2012). These systems, such as Vadalog, have a unique potential to balance the data-driven, deterministic, and explainable nature of deductive reasoning with the flexibility of modern natural language processing (“strings, not things,” as we might say), which often fails, especially when addressing enterprise questions requiring data and knowledge not available in established public databases. Finally, the talk will offer a chance to comment on the challenges an R&D team faces when spinning off production-ready projects from core database research, which, despite the difficulties, often results in high user satisfaction due to the unique qualities of logic-based approaches, such as explainability, scalability, and the creation of valuable enterprise data assets.
Bio
Luigi Bellomarini is a manager, researcher, and software engineer, currently serving as the Head of the Applied Research Team (the R&D Unit, in the IT Directorate General) of the Central Bank of Italy. He is also a Contract Professor of Database Systems at Roma Tre University and a Guest Lecturer on Knowledge Graphs at the University of Oxford. Additionally, he is affiliated with the Center for AI and Machine Learning (CAIML) at TU Wien, an inter-faculty center that brings together researchers in AI and ML, where he leads the Industrial Advisory Board of the SIG Knowledge Graphs group. He holds a Ph.D. in Computer Science and Engineering from the University of Roma Tre and has been a Visiting Scientific Collaborator at the Department of Computer Science at the University of Oxford.
He has a track record of leading R&D projects in both academic and industrial contexts, with a focus on translating theory into practice, particularly in economic, financial, and statistical applications. His research interests encompass a wide range of topics in databases and artificial intelligence, including knowledge-based and logic-based reasoning, neuro-symbolic methods, knowledge graphs, database theory, big data, data integration, and data exchange. He has published his research in leading venues in his fields, including VLDB, IJCAI, ICDE, EDBT, the Journal of Data Semantics, and the Journal of Information Systems.