Knowledge Graphs

Large Language Models demonstrate impressive capabilities in natural language understanding and generation. However, they operate as closed systems trained on static datasets, lacking real-time awareness of new information and struggling with factual accuracy. Two prominent approaches have emerged for RAG: Vector Databases and Knowledge Graphs.

Organisations spend heavily on data lakes, warehouses and cloud migrations and still struggle to get business insight out. A semantic layer — metadata, taxonomy, ontology and knowledge graph — is what closes the gap.

Metcalfe’s Law holds that a network’s value is proportional to the square of its connected nodes. Applied to enterprise data, it explains why every new connection between systems compounds the value of everything already connected.

Chaining six facts together in a relational model can mean an eleven-table join. Graph databases traverse nodes and links instead, sidestepping the mapping tables that make enterprise data so slow to query.