Knowledge Graph Blueprint

The problem

Flat catalogues store records well and answer connected questions badly. A dataset can't be traced to its authors, the instruments behind it, or related work elsewhere without a model the catalogue doesn't have — and the questions that matter most to researchers are exactly the ones it can't answer.

Our approach

We design the graph around the entities and relationships that matter, select or author the ontologies to fit, and wire records to identifiers — internal or external, including DOIs, Wikidata, ARKs, ORCID, and ROR — so the data answers questions and machines can traverse it. Where the scope allows, we build the graph itself, not just the design for one.

What we deliver

• —Schema and ontology selection, extension, and authoring
• —Knowledge graph construction from existing sources
• —ETL pipelines for ingest and continuous synchronisation
• —Reference implementations and query endpoints (SPARQL, GraphQL, JSON-LD)
• —Curation and quality monitoring in operation

How we work

The Basic package delivers the model and a reference pattern. The Advanced package builds on that by constructing the graph itself — so the semantic layer is not just specified but running. An FDO variant applies the same approach on tighter scope, modelling FAIR Digital Objects as the unit of structure.