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