knowMaybe

KnowMaybe is a conceptual framework and software toolkit for representing, measuring, and reasoning about uncertain knowledge. It provides a formal model in which propositions carry probabilities and confidence measures, enabling both deterministic and probabilistic inference within knowledge bases, decision-support systems, and scientific workflows. The goal is to support transparent reasoning when information is incomplete or ambiguous.

The framework comprises a knowledge graph layer that encodes entities, claims, and provenance; a probabilistic reasoning engine that propagates uncertainty through rules and queries; an explanation component that outputs human-readable rationales for conclusions; and a developer API that facilitates integration with databases, data streams, and external services. It emphasizes uncertainty propagation, versioning of knowledge states, and provenance tracking.

KnowMaybe emerged from interdisciplinary work in information science and artificial intelligence on uncertain knowledge. Community-driven implementations and discussions have continued since the early 2020s, with ongoing updates and extensions. It targets researchers, data scientists, policymakers, and organizations seeking structured uncertainty management in decision-making and hypothesis evaluation.

Analysis in academic and practitioner communities notes its modular architecture and emphasis on explainability, while highlighting challenges in scalability for large graphs and a learning curve for users new to probabilistic reasoning.

See also: probabilistic programming, knowledge graph, Bayesian network, explainable AI.