Move from a knowledge domain to questions, concept model, schema, and validation rules.
A verifiable model slice with competency questions, concepts, schema, and example queries.
You can decide which knowledge slice is built and validated first as a knowledge graph or GraphRAG input.
Validate with sample data against shapes and test example queries across competency questions.
Which domain questions must the ontology answer with defined data and inferences?
The process starts with testable questions so the model is not only formally consistent but also measurable on real use cases.
The questions then drive concepts, relations, and example instances in the next step.
Which concepts and propositions answer our focus question?
Concept Mapping makes language, terms, and relationships jointly negotiable before creating a technical schema.
Key concepts and relationships are then translated into graph structures.
Which graph schema answers the key queries correctly, understandably, and efficiently?
The schema translates the domain model into a concrete graph structure with nodes, edges, attributes, and access paths.
The schema then needs rules so data quality and modeling assumptions remain verifiable.
Which RDF structure must hold for the use case, and how is each violation reported?
Shape-first modeling records which structure, required fields, and relationship patterns define valid data.
With shapes, the first model slice can be tested with sample data and queries.
Artifacts stay collapsed until you actually need them.
Plan Competency Questions around its domain steps and the concrete decision question.
# Competency Questions: Worksheet
## Question
To be confirmed
## Desired outcome
To be confirmed
## Scope
Full workshop
Time: 60–90 minutes initially; ongoing test maintenance
Complete this worksheet on paper or in your own document during the work.
## Preparation for this scope
### Method setup
- **Materials:** Use cases and decision questions, domain vocabulary, representative instance data, shared workspace, source links, and decision log.
- **Roles:** Domain expert · ontology engineer · data consumer
- **Advance information:** Collect Use cases and decision questions, domain vocabulary, representative instance data in advance and mark open assumptions.
- **Overall time needed:** 60–90 minutes initially; ongoing test maintenance
- **Setup:** Prepare translate a natural-language domain question into terms, formal query, test data, and expected answer visibly and calibrate assessment terms before starting.
### Prepare for the work steps
#### Elicit question: Elicit question
- Use cases and decision questions
- domain vocabulary
- representative instance data
#### Specify: Specify
- CQ backlog
- domain vocabulary
- representative instance data
#### Formalise: Formalise
- precise CQ
- domain vocabulary
- representative instance data
#### CQ tests: CQ tests
- query test
- domain vocabulary
- representative instance data
## competency question test suite
### Competency question
User · question · decision
...
### Semantics
Terms · quantifier · horizon
...
### Test case
Data · expected answer · negative case
...
### Formalisation
Query · axioms · version
...
Enter only real input, sources, and decisions.
[Grüninger and Fox: Methodology for the Design and Evaluation of Ontologies](https://www.eil.utoronto.ca/wp-content/uploads/enterprise-modelling/papers/gruninger-ijcai95.pdf)
## Elicit question: Elicit question
Expected artifact: CQ backlog
CQ backlog · source · uncertainty · next decision
Entry:
...
- [ ] Is the question relevant to a concrete user?
## Specify: Specify
Expected artifact: precise CQ
precise CQ · source · uncertainty · next decision
Entry:
...
- [ ] Can a unique example answer be given?
## Formalise: Formalise
Expected artifact: query test
query test · source · uncertainty · next decision
Entry:
...
- [ ] Does the formal query preserve the domain question?
## CQ tests: CQ tests
Expected artifact: CQ test suite
CQ test suite · source · uncertainty · next decision
Entry:
...
- [ ] Does the test fail when a core axiom is missing?
## Open questions and next steps
...
Method guide: https://methodatlas.meierhoff-systems.de/en/methods/competency-questions/run-sheet
Plan Property Graph Schema Design around its domain steps and the concrete decision question.
# Property Graph Schema Design: Worksheet
## Question
To be confirmed
## Desired outcome
To be confirmed
## Scope
Full workshop
Time: 2 × 90 minutes plus query testing
Complete this worksheet on paper or in your own document during the work.
## Preparation for this scope
### Method setup
- **Materials:** prioritised queries, sample data and identity rules, volume and change assumptions, shared workspace, source links, and decision log.
- **Roles:** Domain expert · graph engineer · query consumer · data steward
- **Advance information:** Collect prioritised queries, sample data and identity rules, volume and change assumptions in advance and mark open assumptions.
- **Overall time needed:** 2 × 90 minutes plus query testing
- **Setup:** Prepare query-driven modelling of nodes, relationships, properties, and constraints visibly and calibrate assessment terms before starting.
### Prepare for the work steps
#### Prioritise queries: Prioritise queries
- prioritised queries
- sample data and identity rules
- volume and change assumptions
#### Model graph: Model graph
- query catalogue
- sample data and identity rules
- volume and change assumptions
#### Define integrity: Define integrity
- graph schema
- sample data and identity rules
- volume and change assumptions
#### Test queries: Test queries
- constraint set
- sample data and identity rules
- volume and change assumptions
## property graph schema
### Query
Question · expected answer · volume
...
### Graph pattern
Nodes · relationships · direction
...
### Integrity
ID · constraint · cardinality
...
### Query plan
Index · traversal · result
...
Enter only real input, sources, and decisions.
[Neo4j Graph Data Modeling Guidelines](https://neo4j.com/docs/getting-started/data-modeling/guide-data-modeling/)
## Prioritise queries: Prioritise queries
Expected artifact: query catalogue
query catalogue · source · uncertainty · next decision
Entry:
...
- [ ] Are read and write patterns included?
## Model graph: Model graph
Expected artifact: graph schema
graph schema · source · uncertainty · next decision
Entry:
...
- [ ] Do relationships express domain verbs?
## Define integrity: Define integrity
Expected artifact: constraint set
constraint set · source · uncertainty · next decision
Entry:
...
- [ ] Do constraints prevent invalid core data?
## Test queries: Test queries
Expected artifact: validated schema
validated schema · source · uncertainty · next decision
Entry:
...
- [ ] Are result and access path correct?
## Open questions and next steps
...
Method guide: https://methodatlas.meierhoff-systems.de/en/methods/property-graph-schema-design/run-sheet