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| Criterion |  Innovation Lean Startup |  Knowledge Modeling Competency Questions |  Knowledge Modeling NeOn Methodology |  Knowledge Modeling Shape-First Modeling |
|---|---|---|---|---|
Purposedifferent | For uncertain business assumptions, the method forces the idea into contact with real market reactions early. It separates wishful picture, assumption, and observable behavior, so that learning becomes faster than planning. This translates uncertainty into measurable insight. | 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. | 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. | 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. |
Complexitydifferent | Medium | Medium | High | Medium |
Timedifferent | Wochen bis Monate je Lernzyklus | 2-4 h | Mehrere Wochen bis Monate | Halber Tag pro Domain Slice |
Participantsdifferent | 2-8 | 2-8 | 2-10 | 1-4 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Ontology Requirements Specification, Reuse Plan, Ontology Modules, Evaluation Report | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation |
Tagsno overlap | LeanStartupValidationMVP | OntologyKnowledge graphScopeRequirementsSemantic | OntologyMethodologyReuseSemanticEngineering | Knowledge graphValidationSemantic |