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| Criterion |  Knowledge Modeling Competency Questions |  Innovation Lean Startup |  Knowledge Modeling Ontology Design Patterns |  Knowledge Modeling Shape-First Modeling |
|---|---|---|---|---|
Purposedifferent | 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. | 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. | Ontology Design Patterns provide reusable building blocks for recurring modeling problems. The method brings structure to the search for clean concepts, relationships, and roles. | 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. |
Complexitysame | Medium | Medium | Medium | Medium |
Timedifferent | 2-4 h | Wochen bis Monate je Lernzyklus | 1-3 h pro Pattern | Halber Tag pro Domain Slice |
Participantsdifferent | 2-8 | 2-8 | 1-6 | 1-4 |
Formatsame | Workshop + async | Workshop + async | Workshop + async | Workshop + async |
Outputdifferent | Competency Question Set, Required Concepts, Test Queries, Coverage Matrix | Hypothesis list, MVPs, Learning reports, Pivot or persevere decision | Selected Patterns, Adapted Schema Fragments, Pattern Documentation | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation |
Tagsno overlap | OntologyKnowledge graphScopeRequirementsSemantic | LeanStartupValidationMVP | OntologyPatternsReuseSemanticModeling | Knowledge graphValidationSemantic |