Understanding key concepts: semantics, ontology, knowledge graph
Semantics: giving meaning to data
Semantics involves structuring and interpreting data so that AI understands what it means, not just how it is organized.
Semantics gives AI the ability to reason with business concepts as they actually exist in the organization.
Ontology: structuring an organization's knowledge
An ontology is an explicit representation of the key concepts in a domain (objects, actions, events, roles, etc.) and the relationships between them.
It is, in a way, the mental map of the company, but in a format that AI can understand.
It describes:
– business entities (customer, machine, procedure, contract, etc.)
– their properties
– their relationships
– their interaction rules
Unlike a data dictionary, an ontology formalizes meaning, not just technical fields.
The knowledge graph: making knowledge usable
The knowledge graph is the living embodiment of ontology.
It organizes data and knowledge in graph form, enabling AI to navigate links, understand relationships, and infer new information.
The Cleyrop method for a reliable business ontology
An ontology generated and enriched by AI
Cleyrop has made a strong technological choice: to natively integrate a semantic business layer into its platform, automatically fed by the organization's data.
The platform ingests all types of data (structured, unstructured, logs, IoT, documents, etc.).
- ensures governance, quality, traceability
- creates a reliable foundation for AI
- automatically generates an initial business ontology
- enriches it use case after use case, organically
This approach allows ontology to become alive and directly operational.
A native semantic layer: a strategic differentiator
A truly useful ontology must be specific to each organization. There is no generic model that works for everyone, nor is there an industrial template capable of capturing the uniqueness of a profession.
Every company has its own vocabulary, processes, constraints, and internal rules.
That's why Cleyrop builds a semantic layer that is fully tailored to each customer's context, based on their actual data and use cases.
The Knowledge Graph as a contextual backbone
The knowledge graph is the living embodiment of ontology.
Analytics use cases, AI Agents, and Enterprise GPT rely on this semantic layer to offer:
- more precision;
- more relevant;
- more explainability;
- more confidence.
This is what allows AI to be aligned with business lines, rather than the other way around.