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Curated by: Ontology Explained: Philosophy and AI (109 videos)
Probability seems fundamental to intellectual rigor, yet modeling it in deterministic systems like the Web Ontology Language (OWL) presents a significant challenge. OWL is designed for propositions that are true or false—full stop—which clashes with real-world uncertainty (like a "30% chance of rain" or "98% data accuracy"). This video explores practical solutions for integrating confidence, credence, and probabilistic scores into your knowledge graph. We use the following techniques: 1. Datatype Properties to connect events to decimals. 2. Named graphs to classify reliability of data sources. 3. RDF*/RDF1.2 for qualifying the probabilities on specific triples. 4. Lists to add a fourth relata, which lets us sneak in a decimal on another triple. We'll also touch on the deep philosophical interpretations of probability, the connection to Causal Diagrams and Bayesian Networks, and look at alternative approaches like PR-OWL and BayesOWL. Learn how to bridge the gap between deterministic logic and the messy, probabilistic reality of your data.