Introduction to Salmon Knowledge Modelling
By the end of the first three modules, participants will have:
- Discovered and reused existing terms and URIs.
- Created clear definitions and documentation for local data.
- Built a mapping table connecting their terms to others’.
Reusing Terms — Search and Integrate Existing Vocabularies
- Controlled vocabularies capture shared meaning of terms.
- Reusing existing URIs improves interoperability and credibility.
- Reuse saves time, avoids duplication, and makes future integration easier.
Documenting Terms — Write Clear, Useful Definitions
- A data dictionary is the bridge between raw data and understanding.
- Good definitions reduce misinterpretation and support machine processing.
- Documentation is both a social and technical task.
Concept Decomposition
- Relationships reveal meaning.
- Decomposing terms uncovers hidden assumptions.
- Mapping across datasets helps identify where vocabularies can be aligned.
- Concept decomposition prepares you for formalization in SKOS and ontology modeling (coming next!).
From Concepts to Semantics — Introducing SKOS
- SKOS helps bridge informal definitions and formal semantics.
- It supports controlled vocabularies that can later evolve into ontologies.
- Creating a schema diagram helps visualize and communicate conceptual structure.
- Reusing terms and clearly defining relationships builds semantic interoperability.
From Terms to Meaning - Framing Knowledge with Competency Questions
- Competency Questions express the intended use of an ontology in natural language.
- They help translate real-world research and management questions into conceptual structures.
- CQs are iterative, evolving as you refine your vocabulary and build your ontology.
- Good CQs are specific, testable, and connected to real data needs.
Bonus SessionOntology Game Workshop
Ontologies go beyond vocabulary—they structure meaning.
Shared semantics make integration and reuse possible.
Even small conceptual differences can block interoperability.