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| Criterion |  Product Discovery Hypothesis Prioritization Canvas |  Knowledge Modeling NeOn Methodology |  Knowledge Modeling Competency Questions |  Product Discovery Experiment Canvas |
|---|---|---|---|---|
Purposedifferent | When many hypotheses compete for attention, it brings order to their learning sequence and importance. It separates problem, assumption, solution, and evidence. The result is captured as a prioritization canvas and a hypothesis backlog. | 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. | Competency Questions translate a domain model into concrete questions that it must be able to answer. The method keeps the model's scope clean and prevents pretty but useless structures. | 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. |
Complexitydifferent | Medium | High | Medium | Low |
Timedifferent | 60-90 min | Mehrere Wochen bis Monate | 2-4 h | 30-60 min |
Participantsdifferent | 3-8 | 2-10 | 2-8 | 1-5 |
Formatdifferent | Workshop | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Prioritization Canvas, Hypothesis Backlog | Ontology Requirements Specification, Reuse Plan, Ontology Modules, Evaluation Report | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Completed Experiment Canvas, Success Metric |
Tagsno overlap | ExperimentsPrioritizationDiscoveryHypothesis | OntologyMethodologyReuseSemanticEngineering | OntologyKnowledge graphScopeRequirementsSemantic | ExperimentsValidationDiscoveryHypothesis |