I help teams turn messy, multi‑source data into connected knowledge powering semantic search, recommendations, analytics, and trustworthy LLMs.
I specialise in ontology engineering, knowledge graph design, semantic data integration, and reasoning technologies,
aligning semantic web standards with AI and data‑driven applications.
Ontology construction, semantic modelling, and alignment to industry and upper ontologies to capture complex domain knowledge in a robust, reusable way.
Design and implementation of knowledge graphs that integrate data from multiple sources and support search, analytics, and decision‑support workflows.
Semantic data integration and reasoning using OWL, RDF, SPARQL and SHACL, ensuring interoperability, consistency, and regulatory compliance.
Combining knowledge graphs, RAG, LLMs and agents to power intelligent information systems and advanced decision‑support across regulated domains.
From requirements and competency questions to formal OWL/SHACL models, SKOS thesauri and domain ontologies aligned with standards such as FIBO, DC, SCHEMA or SUMO.
Design of RDF and property‑graph solutions (Stardog, Neo4j, GraphDB, etc.), including data modelling, identity strategy, inference patterns and SPARQL/Cypher query design.
Mapping heterogeneous data sources into a coherent semantic layer using RDF/RDFS, JSON‑LD and SHACL, with validation rules that protect data quality and consistency.
Designing knowledge‑graph‑aware RAG pipelines and agentic workflows so LLMs can reason over curated domain knowledge instead of unstructured text alone.
Applying reasoning technologies and semantic web standards to support complex decision‑making in healthcare, insurance, legal and defense contexts.
Helping organisations define semantic roadmaps, governance practices and technology choices that connect AI initiatives with robust knowledge engineering.
Engage domain experts, capture use cases and decision points, and collect key information assets and data sources.
Build ontologies and vocabularies that reflect the real language of the business while conforming to semantic web standards.
Implement knowledge graphs, reasoning rules and validation, and connect them to applications, services and analytics.
Establish governance, tooling and practices so the semantic layer can evolve with your organisation and your AI strategy.
Clear models (diagrams + OWL/SHACL), populated graphs, documented APIs/queries, and runbooks for governance and change.
Typical engagement is 6–12 weeks to a working slice: a modeled subdomain, a small but real graph, and at least one integrated use case.
Use the right tool for the job. I work across RDF/SPARQL and LPG/Neo4j, often combining them with search indexes.
Yes—graph‑grounded RAG, retrieval evaluation, and schema‑aware prompting to keep answers accurate and auditable.