Knowledge Engineering Training — Ontologies & Semantic Technologies | Care2Data
Training
Knowledge Engineering Training
From Concepts to Capability — Develop In-House Ontology & Semantic Expertise
Care2Data's Knowledge Engineering training program equips clinical, data, and regulatory teams with the skills to design, implement, and govern ontology-driven knowledge systems — moving organizations from traditional data practices to semantic, reasoning-enabled ways of working.
Training Goals
Building Capability, Not Dependency
Care2Data's Knowledge Engineering training is designed to bridge the gap between traditional data practices and knowledge-based engineering approaches — enabling teams to confidently adopt ontology-driven, semantic methods.
Develop in-house expertise in semantic technologies and ontology-driven knowledge engineering
Enable teams to independently design, build, and govern ontologies, taxonomies, and knowledge models
Bridge traditional data practices with knowledge-based, reasoning-driven engineering approaches
Apply knowledge engineering principles to real clinical and life sciences data challenges
Apply FAIR (Findable, Accessible, Interoperable, Reusable) data principles to ontology and knowledge model design
Build long-term capability to sustain and evolve knowledge-driven systems — aligned to Care2Data's Build, Operate, Transfer (BOT) model
Training Areas
What You'll Learn
Semantic Technologies
Foundations of RDF, OWL, SPARQL, and the semantic web stack — representing data as meaningful, machine-interpretable knowledge.
Ontologies
Ontology design principles — classes, properties, relationships, taxonomies, and controlled vocabularies for domain-specific knowledge.
Models
Conceptual, logical, and knowledge models that structure clinical and scientific data into governed, reusable representations.
Knowledge Modelling
Hands-on knowledge graph construction — mapping relationships and applying inference and reasoning to enable discovery.
Levels of Training
A Structured Learning Path
Foundation
Introduction to semantic technologies and knowledge representation — covering core concepts of ontologies, taxonomies, and the role of knowledge engineering in data-driven organizations.
Practitioner
Hands-on ontology design and knowledge modelling using industry-standard tools — building domain ontologies and knowledge graphs through guided exercises.
Applied / Advanced
Applied knowledge engineering for real-world clinical and life sciences use cases — reasoning, plausibility checks, traceability, validation, governance, and integration into operational systems.
Ready to grow your team's knowledge engineering capability?Get in touch →