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Four ways we help ecosystems understand each other

Ontology engineering, vocabulary governance, taxonomy design and semantic interoperability, for organisations whose data must converge on one shared meaning.

Ontology engineering and knowledge graphs

Ontologies, semantic layers and knowledge graphs that give your data one meaning. We design them from the questions your people need answered and build them over the systems you already run.

Ontology design and engineering · semantic layers · knowledge graphs

Typical deliverables
  • Competency questions that state what the model must answer
  • Ontology and semantic layer design, reusing open ontologies where they fit
  • Knowledge graph proof of concept and evidence-based technology choice
Who it’s for
  • Research and R&D organisations
  • Manufacturers and industry
  • Public administrations and international organisations
From our work
Featured case · Drug discovery research

From platform-hopping to one integrated graph

Challenge

Researchers looking for new drug targets searched several platforms one after another.

Approach

We turned the questions of biology and bioinformatics teams into competency questions, used them to select and validate a knowledge graph platform, and ran the induction that changed how researchers work.

Result

Researchers query one integrated graph, with measured gains in accuracy and less time and effort per investigation.

All work in this area

Vocabulary and metadata governance

Code lists, classifications and thesauri your team can maintain with confidence. We assess them against the FAIR principles, set up the editorial workflow and tools, and train the people who own them.

Health checks · editorial workflows · SKOS · team training

Typical deliverables
  • Health check against quality measures and the FAIR principles
  • Modular architecture and an editorial workflow with sign-off
  • Indicators anyone can verify from your published files
  • Tooling, manuals and training for the team that will own it
Who it’s for
  • Statistics offices and public agencies
  • International organisations
  • Research infrastructures
From our work
Featured case · National statistics

Tax classifications, maintained in-house

Challenge

A national statistics office needed a catalogue of tax-reporting classifications that its own team could maintain.

Approach

We designed a modular architecture from code lists to SKOS vocabularies, set up the editing, publishing and storage tools, turned existing spreadsheets into linked classifications, and built an editorial workflow from reviewer feedback to approved publication.

Result

Workshops and step-by-step manuals prepared a team of eight to create and maintain the catalogue.

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Taxonomy and classification design

Business classification schemes and taxonomies built from what your people know. We gather it through interviews and focus groups, structure it from top concepts down, and set up its governance.

Knowledge elicitation · classification schemes · taxonomies

Typical deliverables
  • Interviews, focus groups and a data inventory
  • A classification scheme or taxonomy, from top concepts down
  • Governance and a platform for shared maintenance
Who it’s for
  • Development finance and international organisations
  • Bodies building a shared taxonomy across disciplines
  • National and regional institutions
From our work
Featured case · Development finance

One classification for a continent of offices

Challenge

A multilateral development bank needed one way to classify the records produced by its daily work.

Approach

Focus groups and interviews across its offices captured the business from functions to transactions. We consolidated it into one business classification scheme and file plan, defined its governance and selected the platform to maintain it.

Result

The scheme is the main structure for filing and archiving the bank’s records.

All work in this area

Semantic interoperability and data spaces

Data that crosses organisations and borders with its meaning intact. We bring interoperability into the first design decision, where it costs least: we align your models with what your ecosystem already adopts, prove the approach in data space pilots, and measure convergence with the EVIDENT framework.

Data spaces · shared standards · convergence measurement

Typical deliverables
  • Data space pilots and proofs of concept
  • Interoperability requirements written next to your competency questions
  • Model choices backed by evidence of adoption among the participants you exchange data with
  • Convergence measurement and experience transfer between participants
Who it’s for
  • EU institutions and national administrations
  • Data space operators and participants
  • Consortia delivering EU projects
From our work
Featured case · EU public administrations

Shared vocabularies, proven in real data spaces

Challenge

European public administrations needed to see shared vocabularies work in real data spaces.

Approach

Inside an EU-level semantic interoperability programme, we designed and supervised data space pilots in environment and mobility, monitored the digitalisation maturity of national administrations, and ran pairing exercises between member states.

Result

Working demonstrations of semantic interoperability, and experience passed from one member state to another.

All work in this area

How we work together

Advisory

Senior input on architecture, governance or standards, on a retainer or per question.

Project delivery

Leading the delivery of a registry, model or platform as PM, tech lead or product owner.

Consortium partner

Joining a bid or consortium as the semantic interoperability specialist.

Questions

Who do you typically work with?

EU institutions, national administrations, international organisations, research infrastructures and industry teams whose data must share meaning across organisations.

Can you join our bid or consortium?

Yes. We join as the semantic interoperability partner, from proposal writing to delivery.

How does an engagement start?

With a free 30-minute call about your data and what has to change. Then we send a short written proposal with the scope, the steps and the cost.

How do you choose which models to reuse?

By evidence. We look at who maintains a model, how authoritative it is, whether it runs in production and which of the participants you exchange data with already use it.

Do you work with our existing tools and standards?

Yes. We build on what you already run and on established standards such as SKOS, OWL and the EU core vocabularies, so nothing gets locked into a tool of ours.

Working on a data space, a registry or a standard? Let’s talk.