Ontology Maker
Creating shared understanding for complex systems.
Lead development
Partners: Cross-sector research and industry partners
Outputs: Glossaries & taxonomies, Knowledge graphs & ontologies, Competency & governance frameworks
What is Ontology Maker?
Ontology Maker is Visioning Lab's methodology and AI-assisted platform for helping organisations create a shared understanding of complex topics.
Whether the challenge is water regulation, disaster recovery, energy transition, neurotechnology governance, systems engineering, or emerging technologies, organisations often struggle because people use the same words differently. Engineers, policymakers, researchers, regulators and communities may all understand key concepts in different ways.
Ontology Maker helps organisations capture, structure and agree their terminology, concepts and relationships. The result is a shared knowledge framework that can be used for decision-making, standards development, training, AI systems, knowledge management and stakeholder engagement.
Unlike traditional ontology engineering approaches, Ontology Maker combines ethnographic research, facilitated workshops, AI-assisted analysis and knowledge modelling. This allows domain experts to participate directly in creating and validating the knowledge structures that describe their world.
What problems does it solve?
Ontology Maker is particularly useful where:
- Multiple organisations need to collaborate across different disciplines
- Regulations, standards or policies require consistent interpretation
- Knowledge is distributed across reports, meetings and stakeholder groups
- Organisations want to prepare data for AI and knowledge graph applications
- New technologies are emerging faster than agreed terminology
- Lessons learned from projects and case studies need to be captured and reused
Typical outputs include:
- Glossaries
- Taxonomies
- Competency frameworks
- Knowledge graphs
- Ontologies
- Decision-support models
- Metadata specifications
- Standards and governance frameworks
How Ontology Maker works
Capture — Gather knowledge from documents, workshops, interviews, standards and case studies.
Extract — Identify concepts, terms, actors, decisions, processes and relationships.
Structure — Organise concepts into categories, hierarchies and knowledge models.
Validate — Review findings with stakeholders and subject matter experts.
Publish — Create glossaries, taxonomies, knowledge graphs, case study repositories or formal ontologies.
Reuse — Enable AI search, decision support, reporting and future standards development.
Example projects
Ontology Maker is already proven across multiple sectors.
| Sector | Example |
|---|---|
| Water | Section 82 Continuous Water Quality glossary |
| Water | Leakage terminology framework |
| Disaster recovery | Nepal Red Cross training glossary |
| Temporary architecture | Disaster recovery knowledge model |
| Energy | Battery Passport glossary |
| Neurotechnology | TIPSS governance ontology |
| Systems thinking | Competency framework ontology |
| Metaverse | Community glossary and ontology |
Case study knowledge systems
Ontology Maker is increasingly being used to transform collections of case studies into structured knowledge systems.
Rather than treating each case study as a standalone report, Ontology Maker identifies recurring themes, actors, decisions, interventions, outcomes and lessons across multiple projects.
This creates a searchable and reusable knowledge base that allows organisations to:
- Compare projects
- Identify patterns and best practice
- Capture tacit knowledge
- Support evidence-based decision making
- Prepare information for AI and knowledge graph applications
Our Water Decisions demonstrator shows how case studies can be represented using a common framework, enabling users to explore decisions, evidence, stakeholders and outcomes across a portfolio of projects.
Why Visioning Lab?
Ontology Maker was developed through real-world projects rather than purely academic research.
The methodology combines:
- Social anthropology and ethnography
- Systems thinking
- Ontology engineering
- Standards development
- AI-assisted knowledge extraction
- Participatory stakeholder engagement
We believe ontology development is not simply a technical exercise. It is a process of building shared understanding between people, organisations and disciplines. This means successful ontologies emerge through collaboration and consensus-building as much as through technology.