Summary
- Profile Type
- Technology offer
- POD Reference
- TOGB20250703025
- Term of Validity
- 3 July 2025 - 3 July 2026
- Company's Country
- United Kingdom
- Type of partnership
- Commercial agreement with technical assistance
- Research and development cooperation agreement
- Targeted Countries
- All countries
Contact the EEN partner nearest to you for more information.
Find my local partner
General information
- Short Summary
- A UK-based innovation SME offers a platform that structures institutional knowledge into machine-readable ontologies, taxonomies and metadata formats, enabling explainable AI, scalable knowledge graphs, and standards-compliant data governance. Proven with UK infrastructure operators. Seeks research or commercial partnerships, including Horizon Europe collaborations on AI, metadata standards, or secure data infrastructure.
- Full Description
-
The company has developed a modular platform designed to orchestrate, simplify and align institutional knowledge across large or distributed systems. The platform transforms human domain knowledge into machine-readable formats (Resource Description Framework (RDF), Simple Knowledge Organisation System (SKOS), JavaScript Object Notation for Linked Data (JSON-LD)) enabling semantic enrichment, searchability, and AI-readiness without requiring specialist users.
Originally developed to support public-sector transformation and digital architecture, the platform is now being scaled for use in critical infrastructure (e.g. energy, water, transport) where explainability, regulatory compliance, and resilient decision-making are paramount. The SME has delivered multi-year knowledge structuring projects for UK organisations, including national infrastructure operators and large industrial clients, visualising and interconnecting over 50 bespoke ontologies, taxonomies and data models at one operator alone. This work has improved knowledge reuse, accelerated onboarding, and reduced cognitive and operational friction.
The platform enables:
• Machine-readable knowledge transformation from expert narratives and legacy documents
• Conceptual weighting and hierarchical simplification to align multiple user perspectives
• Automated outputs in open, interoperable formats (RDF, OWL, SKOS, JSON-LD, UML)
• Support for FAIR principles, explainability, and evolving EU compliance mandates
Its technical capabilities align closely with upcoming Horizon Europe Cluster 4 priorities, particularly:
• HORIZON-CL4-2026-04-DATA-06: Automating metadata tagging, ontology discovery, annotation, and quality control for adaptive data systems
• HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61: Contributing to standards for data formats, taxonomies, and ontologies in scientific AI foundation models
The UK SME’S contribution to the relevant Horizon Europe topics mentioned above would be:
1) HORIZON-CL4-INDUSTRY-2025-01-DIGITAL-61 – AI Foundation Models in Science (GenAI4EU)
Relevant expected output: “Contribute to efforts to reach common standards for data formats, metadata, taxonomies and ontologies.”
SME Platform contribution:
• Generate and visualise taxonomies and ontologies using RDF, OWL, SKOS, JSON-LD.
• Bridge domain silos through structured, weighted knowledge models.
• Enable FAIR-compliant, machine-readable inputs for model training and adaptation.
• Support standardised documentation and schema development for benchmarking and validation.
2) HORIZON-CL4-2026-04-DATA-06 – Efficient and Compliant Use of Data (AI, Data and Robotics Partnership – Area 2)
Relevant scope: Automate key data processes such as curation, tagging, ontology discovery, annotation, and quality control.
SME Platform contribution:
• Automates metadata and ontology generation, reducing manual effort and inconsistency.
• Provides curation and quality workflows for annotation and semantic fit-for-purpose datasets.
• Ensures interoperability with open linked data outputs across sectors.
• Improves traceability and regulatory alignment in sensitive data contexts.
• Supports semantic frameworks for synthetic data generation or federated architectures.
The SME is open to research cooperation where its platform can support semantic structuring, metadata pipelines, ontology alignment, or compliance architecture. This includes support for large-scale foundation model training, provided project scope includes semantically enriched, standards-based data management tasks. - Advantages and Innovations
-
• Semantic automation without coding: Users can produce linked data outputs (RDF, OWL, SKOS, JSON-LD) without specialist expertise.
• Cognitive-first design: Outperforms traditional taxonomy tools in findability, reuse, and user comprehension.
• Explainability and compliance: Outputs align with and support EU requirements under the AI Act and Data Governance Act.
• Reduced reliance on specialist roles: Designed to be used by public and private teams without deep ML/AI skills.
• Proven outcomes: Delivered significant cost reductions in structured knowledge reuse and improved clarity in decision workflows. - Stage of Development
- Available for demonstration
- Sustainable Development Goals
- Goal 9: Industry, Innovation and Infrastructure
- Goal 8: Decent Work and Economic Growth
- Goal 10: Reduced Inequality
- Goal 17: Partnerships to achieve the Goal
- IPR status
- Secret know-how
Partner Sought
- Expected Role of a Partner
-
Type of Partner Sought:
Research organisations, universities, infrastructure operators, AI and data science consortia, semantic web developers, and regulatory or public-sector bodies seeking to build consortia for upcoming Horizon Europe Cluster 4 calls.
Expected role of the Partner:
• Consortium member or project coordinator seeking modular platform input for metadata management, semantic integration, explainability, or compliance infrastructure.
• Provide domain expertise, datasets, or applied research contexts aligned with open science, foundation models, or FAIR data ecosystems.
Task to be performed by the partner:
• Integrate platform as a metadata engine or semantic tool in Horizon Europe projects
• Collaborate on explainability, compliance, or knowledge infrastructure for AI systems
• Co-develop domain-specific taxonomies and schemas using platform tooling
Co-operation envisaged under commercial agreement or research co-operation agreement. - Type and Size of Partner
- Big company
- University
- R&D Institution
- SME <=10
- SME 11-49
- Other
- SME 50 - 249
- Type of partnership
- Commercial agreement with technical assistance
- Research and development cooperation agreement
Dissemination
- Technology keywords
- 01003006 - Computer Software
- 01003010 - Databases, Database Management, Data Mining
- 01003003 - Artificial Intelligence (AI)
- 01003013 - Information Technology/Informatics
- Market keywords
- 02006005 - Big data management
- 02006004 - Data processing, analysis and input services
- 02007020 - Artificial intelligence programming aids
- 02007021 - Other Artificial intelligence related
- Sector Groups Involved
- Digital
- Targeted countries
- All countries