Knowledge graph: definition, value and limits
Knowledge graph Updated
Definition
A knowledge graph is a data model that represents concepts and objects as nodes and their relationships as edges. Instead of keeping data in separate tables, it links customers, products, contracts and cases into a searchable network. In process automation it gives an AI step context so it can answer questions about connected facts, without everything having to sit in a single system.
Why the Term Matters
Company knowledge often sits scattered across SAP, CRM, file stores and people's heads. An AI step that only sees a text does not know these connections. A knowledge graph makes relationships explicit, such as which contract belongs to which customer and which asset. Who understands the term gives the automation a solid memory instead of guessing anew on every request.
When It Is Relevant
- Answers depend on relationships between objects, not on a single value.
- Knowledge sits in several systems and should be queryable together.
- An AI step should use context instead of hallucinating freely.
- Connections change and must be maintained traceably.
When Not
For a simple flow with clear fields in one system a knowledge graph is too heavy. And when nobody maintains the model and the relationships, it ages quickly and causes more confusion than value, so a plain database stays the more honest choice.
Industries That Use It
- Plant engineering: connect components, assets, contracts and maintenance into a queryable network.
- Legal advisory: make clients, cases, deadlines and documents visible in their relations.
- Pharma and research: link substances, studies and regulations across sources.
- IT services: map systems, owners and dependencies for service automation.
Typical Use Cases
- Give an AI assistant context so it answers connected facts correctly.
- Make relations between customer, contract and asset visible for a review.
- Merge scattered knowledge from several systems into a queryable network.
- Model dependencies in a process before it is automated.
- Detect duplicate or contradictory entries through relationships.
Decision Guide
| Signal | Knowledge graph fits | Alternative |
|---|---|---|
| Answer depends on relations | model objects and edges | a table of single values is enough |
| Knowledge across many systems | build a linked network | a point-to-point query |
| AI needs context | graph as knowledge base | a plain text prompt |
| No maintenance possible | a light database | no graph without upkeep |
Example
A mid-market company wants an AI assistant for asset maintenance. A knowledge graph links assets, components, maintenance contracts and contacts from ERP and file store. When someone asks about due maintenance for an asset, the assistant follows the relationships in the graph instead of guessing. The graph is maintained on change. This describes the method, not an asserted client metric.
Service Connection
A knowledge graph usually emerges within a process automation with AI or a process automation, and the framing comes from the process automation consulting.
Sources
This framing describes common practice for using knowledge graphs in automation projects. Concrete implementation, modelling and data protection depend on the case, the systems and the tools and must be checked case by case.