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| Criterion |  Knowledge Modeling Shape-First Modeling |  Innovation Lean Startup |  Knowledge Modeling NeOn Methodology |  Knowledge Modeling Competency Questions |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | 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. |
Complexitydifferent | Medium | Medium | High | Medium |
Timedifferent | Halber Tag pro Domain Slice | Wochen bis Monate je Lernzyklus | Mehrere Wochen bis Monate | 2-4 h |
Participantsdifferent | 1-4 | 2-8 | 2-10 | 2-8 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Ontology Requirements Specification, Reuse Plan, Ontology Modules, Evaluation Report | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix |
Tagsno overlap | Knowledge graphValidationSemantic | LeanStartupValidationMVP | OntologyMethodologyReuseSemanticEngineering | OntologyKnowledge graphScopeRequirementsSemantic |