Give your data semantic meaning. SolveBI designs and implements enterprise ontologies, Digital Twin models, and knowledge graphs on Microsoft Fabric - enabling AI grounding, semantic search, and unified data understanding across your organisation.
An ontology is a formal, machine-readable model of a domain's concepts, properties, and relationships. In Microsoft Fabric, ontologies provide the semantic layer that gives raw data meaning - defining what a "customer", "asset", or "transaction" is, how they relate, and what rules govern them.
Within the Fabric ecosystem, ontologies are most commonly expressed using Digital Twins Definition Language (DTDL) for physical world modelling, or using RDF/OWL for semantic web and knowledge graph applications. Both connect to your Fabric data through Graph Instances, Lakehouses, and Azure Digital Twins.
As AI and large language models (LLMs) become central to enterprise analytics, a well-defined ontology acts as the grounding layer that ensures AI responses are accurate, consistent, and aligned with your organisation's specific domain knowledge.
Ontology is one of the most technically demanding areas of enterprise data management. Our consultants combine domain knowledge with Fabric expertise to deliver semantic layers that actually work in production.
We work with your domain experts to define entities, relationships, properties, and constraints that accurately model your business or operational domain in a machine-readable ontology.
Implement Digital Twins Definition Language (DTDL) ontologies for manufacturing, energy, smart buildings, and infrastructure - integrating with Azure Digital Twins and Fabric.
Build and populate knowledge graphs from structured and unstructured sources, connecting your ontology definitions to real-world data stored in Fabric Lakehouse or Warehouse.
Enable semantic search over your data estate using ontology-driven indexing, so users can find information by meaning and context rather than exact keywords.
Ground large language model (LLM) applications in your organisational ontology to reduce hallucinations, improve answer accuracy, and enforce governance on AI-generated outputs.
Align your ontology with industry standards (IEC CIM, W3C OWL, DTDL, schema.org) and integrate with Microsoft Purview for unified data governance across your Fabric environment.
Model physical assets, sensors, production lines, and their relationships using DTDL ontologies that feed real-time Fabric analytics and predictive maintenance workflows.
Represent grid topology, generation assets, and distribution networks semantically - enabling impact analysis, outage prediction, and regulatory compliance reporting.
Build an organisational knowledge graph that connects products, customers, contracts, and processes, enabling AI-augmented search and recommendation across your data estate.
Define clinical ontologies aligned with FHIR, SNOMED, or proprietary schemas that unify patient, treatment, and outcome data across disparate systems.
Talk to a SolveBI consultant about building the ontology and knowledge graph foundation your AI and analytics initiatives need.