Beyond True/False: A Practical Guide to Probabilistic Modeling in OWL and RDF

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.