Knowledge Graph

What is a knowledge graph?

A knowledge graph is a structured representation of entities and the relationships between them. Rather than storing information as documents or table rows, it records that a specific part fits a specific vehicle configuration, that a supplier is a subsidiary of a parent company, or that a deviation relates to a batch, a piece of equipment, and a corrective action.

The value is in the relationships. Retrieval over documents finds passages that resemble a query. A knowledge graph answers questions that require traversing connections: which products are affected by a change to this component, which submissions reference this study, which accounts belong to this corporate group.

Knowledge graphs and semantic retrieval solve different problems and work well together. Retrieval handles meaning in unstructured text. A graph handles precise, multi step relationships that similarity cannot express reliably.

When does an enterprise need a knowledge graph?

Four signals indicate that document retrieval alone will not be sufficient.

Multi hop questions are common. Questions requiring several connected lookups, where each answer determines the next step. Retrieval answers these unreliably because no single passage contains the chain.

Entity identity is fragmented. The same organisation, product, or component appears under different identifiers across systems, and resolving that identity is itself the hard part of the work.

Relationships carry consequences. In regulated and engineering contexts, what connects to what determines impact scope. Getting the relationship wrong is more serious than returning an imprecise passage.

The same entities are referenced constantly across processes. Where a stable set of entities recurs, the cost of modelling them explicitly is repaid across every use case that touches them.

The cost is real: building and maintaining a graph requires ongoing data engineering, and a graph that drifts out of date produces confident wrong answers with more authority than a stale document does.

Where structured relationships matter in Wizr AI solutions

Two areas of Wizr AI’s portfolio depend heavily on explicit entity relationships rather than on text similarity alone.

In automotive operations, Global Parts Catalog Intelligence performs automated part number normalisation and usage mapping, which is entity resolution and relationship modelling in substance: determining that regional variants refer to the same part, and which vehicle configurations each component applies to. Smart Catalog Synchronisation then propagates those relationships across global markets.

In pharma and biotech, the Competitor Approval Tracker and RLD Label Change Monitor both depend on resolving manufacturers and products across fragmented regulatory sources where corporate names and product designations vary by jurisdiction.

The underlying data work sits within Enterprise AI Services as data engineering, covering the integration, optimisation, and classification of data for use by AI models.