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