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| Criterion |  Knowledge Modeling NeOn Methodology |  Product Discovery Experiment Canvas |  Product Strategy DIBB |  Product Discovery Riskiest Assumption Test |
|---|---|---|---|---|
Purposedifferent | The NeOn Methodology structures ontology work as a controlled process with reuse, scenarios, and reviews. It is useful when ontologies should not emerge ad hoc but modularly and reusably. | When experiments become unreadable after the fact, it arranges hypothesis, signal, and learning goal on a single canvas. It separates problem, assumption, solution, and evidence. The result is captured as a Completed Experiment Canvas and a Success Metric. | DIBB helps clarify target groups, value, goals, and priorities by making the logic behind a decision explicit. It captures results as a DIBB document, belief list, bet list, and learning report. | When an idea hinges on one critical unknown, it prioritizes testing that exact piece of uncertainty first. It separates problem, assumption, solution, and evidence. The result is captured as a prioritized list of assumptions, a test plan, and a result report. |
Complexitydifferent | High | Low | Low | Medium |
Timedifferent | Mehrere Wochen bis Monate | 30-60 min | 1-2 h | 1-2 Wochen pro Iteration |
Participantsdifferent | 2-10 | 1-5 | 2-8 | 2-6 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Ontology Requirements Specification, Reuse Plan, Ontology Modules, Evaluation Report | Completed Experiment Canvas, Success Metric | DIBB document, Belief list, Bet list, Learning report | Prioritized Assumption List, Test Plan, Results Report |
Tagsno overlap | OntologyMethodologyReuseSemanticEngineering | ExperimentsValidationDiscoveryHypothesis | StrategyDecisionAssumptionsHypothesis | ExperimentsValidationDiscoveryAssumptions |