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| Criterion |  Product Discovery Smoke Test |  Knowledge Modeling Shape-First Modeling |  Knowledge Modeling NeOn Methodology |  Product Discovery Fake Door Test |
|---|---|---|---|---|
Purposedifferent | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | Shape-First Modeling defines data quality through shapes before implementation or integration frays at the edges. The approach fits when validation and data contracts should be part of the design from the start. | 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 demand is still unclear, it measures interest in a feature before it is built. It separates real behavior from polite agreement and deliberately keeps build effort small. |
Complexitydifferent | Low | Medium | High | Medium |
Timedifferent | 1-5 Tage | Halber Tag pro Domain Slice | Mehrere Wochen bis Monate | 1-5 Tage |
Participantsdifferent | Nutzertraffic | 1-4 | 2-10 | Nutzertraffic |
Formatdifferent | Async | Workshop + async | Workshop + async | Async |
Outputdifferent | Interest Metrics, Conversion Signal, Learning Note | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Ontology Requirements Specification, Reuse Plan, Ontology Modules, Evaluation Report | Click Data, Interest Signal, Learning Decision |
Tagsno overlap | ValidationExperimentsDemandGrowth | Knowledge graphValidationSemantic | OntologyMethodologyReuseSemanticEngineering | ValidationExperimentsDemandDiscovery |