View methods side by side.
Choose up to four methods. Add them using the search and share the comparison by copying its link.
| Criterion |  Knowledge Modeling Competency Questions |  Innovation Lean Startup |  Knowledge Modeling Shape-First Modeling |  Knowledge Modeling NeOn Methodology |
|---|---|---|---|---|
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. | 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. | 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. |
Complexitydifferent | Medium | Medium | Medium | High |
Timedifferent | 2-4 h | Wochen bis Monate je Lernzyklus | Halber Tag pro Domain Slice | Mehrere Wochen bis Monate |
Participantsdifferent | 2-8 | 2-8 | 1-4 | 2-10 |
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 | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Ontology Requirements Specification, Reuse Plan, Ontology Modules, Evaluation Report |
Tagsno overlap | OntologyKnowledge graphScopeRequirementsSemantic | LeanStartupValidationMVP | Knowledge graphValidationSemantic | OntologyMethodologyReuseSemanticEngineering |