Expertise
I work on semantic systems where the main difficulty is not only data exchange, but agreement about meaning: terms, categories, relations, constraints, mappings, and the assumptions behind them.
My expertise sits across five connected areas:
- Ontology engineering and conceptual modeling
- Knowledge representation and Semantic Web implementation
- Semantic artifacts and metadata resources
- Semantic interoperability and mappings
- Tooling, validation, and documentation workflows
Core areas
Ontology engineering and conceptual modeling
I design and review conceptual and formal models that make domain semantics explicit. This includes domain analysis, conceptual clarification, ontological analysis, and the use of modeling languages and foundational ontologies such as OntoUML, UFO, and gUFO.
Typical work includes:
- Identifying and refining domain concepts, relations, roles, phases, kinds, events, and constraints
- Reviewing conceptual models for ontological consistency and semantic precision
- Connecting conceptual models to computational representations
- Documenting modeling decisions and intended interpretations
Knowledge representation and Semantic Web implementation
I work with machine-processable semantic artifacts using RDF, RDFS, OWL, SHACL, SPARQL, and related Semantic Web technologies.
Typical work includes:
- Designing OWL ontologies and RDF vocabularies
- Defining SHACL constraints for validation and quality control
- Representing conceptual distinctions in computable forms
- Aligning semantic models with knowledge-graph and linked-data publication needs
- Using SPARQL for inspection, querying, and quality checks
Semantic artifacts and metadata resources
I work on reusable semantic resources that support interoperability and governance.
Typical work includes:
- Structuring ontologies, controlled vocabularies, taxonomies, thesauri, and lexicons
- Organizing data and model catalogs
- Maintaining mapping sets and metadata schemas
- Documenting the scope, intended use, and interpretation of semantic resources
- Aligning semantic resources with relevant standards and vocabularies, including SKOS, OntoLex-Lemon, DCAT, Dublin Core Terms, MOD, and SSSOM
Semantic interoperability and mappings
I work on interoperability problems where different systems, standards, models, or communities use different structures for related meanings.
Typical work includes:
- Clarifying semantic overlap and mismatch between models
- Specifying meaning-level mappings
- Documenting mapping assumptions and limitations
- Designing traceability between conceptual models, computational ontologies, vocabularies, and implementation artifacts
- Supporting FAIR-aligned publication and reuse of semantic resources
Tooling, validation, and documentation workflows
I use Python, Git, GitHub, and automation practices to support semantic-engineering work.
Typical work includes:
- Prototyping semantic-processing workflows
- Generating or validating semantic artifacts
- Maintaining versioned repositories for ontologies, vocabularies, mappings, and documentation
- Improving reproducibility and traceability through structured files, scripts, and continuous integration
- Writing technical documentation for specialist and mixed technical audiences
Technical stack
| Area | Tools and technologies |
|---|---|
| Ontology and conceptual modeling | UFO, gUFO, OntoUML, UML |
| Semantic Web | RDF, RDFS, OWL, SHACL, SPARQL, Linked Data |
| Semantic resources | SKOS, OntoLex-Lemon, DCAT, Dublin Core Terms, MOD, SSSOM |
| Development and automation | Python, Git, GitHub, CI workflows |
| Modeling and ontology tools | Protégé, Visual Paradigm, Astah, Enterprise Architect |
| Documentation | Markdown, MkDocs, LaTeX/Overleaf, technical reports |
Role fit
This background is most relevant to senior technical roles such as:
- Ontology Engineer
- Knowledge Engineer
- Semantic Web Engineer
- Knowledge Graph Engineer
- Semantic Interoperability Specialist
- Metadata and Semantics Specialist
- Semantic / Information Architecture Specialist
My primary contribution is not generic data engineering, frontend development, or machine-learning model development. It is the design, implementation, validation, and governance of formal semantic structures that make complex domains more explicit and interoperable.