Data Mining Meets Logic: Situation-Based Modal Logic and Metadata Veracity - Simone Cuconato (Science & Philosophy, 2025)
Logic and theoretical computer science are deeply interconnected, with logic forming a foundational pillar in the emergence of computer science. This connection has grown stronger over time, driven by advancements in symbolic systems within artificial intelligence, formal verification methods, and automated reasoning techniques. However, within the vast landscape of computer science, data science remains an area where the link to logic is relatively underdeveloped. In this paper, I apply algebraic logic to address an urgent challenge for data scientists: metadata veracity. Inspired byWillard Van Orman Quine’s well-known slogan, “No entity without identity”, I propose, “No data without metadata”, underscoring the importance of descriptive metadata in scientific articles. The central idea of my framework is that the geometric zones of a scientific article – with its atomic metadata – , correspond to an algebraic situation space. Consequently, I develop a situation-based semantics grounded on the idea that situations are portions of a possible world, a possible world is a PDF document and that sentences point to situations and describe what is going on in them. Applying mathematical logic tools to data mining enables a rigorous framework for defining metadata veracity, offering a structured approach to assess the accuracy and reliability of extracted information.