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IJONIS
Data & Infrastructure

Knowledge Graph

A knowledge graph is a data structure that represents knowledge as a network of entities (nodes) and their relationships (edges). It models connections between customers, products, processes, and documents as they exist in the real business world — enabling AI systems to answer complex questions across multiple relationships.

Why does this matter?

Knowledge graphs make implicit enterprise knowledge explicit and machine-usable. "Which suppliers deliver components used in recalled products?" — such questions spanning multiple data sources are answered by a knowledge graph in seconds. Combined with RAG, they significantly improve AI system response quality.

How IJONIS uses this

We build knowledge graphs with Neo4j and property graph models fed directly from your enterprise data (ERP, CRM, document management). For AI applications, we combine the graph with vector databases — so RAG systems benefit from both semantic similarity and structural relationships.

Frequently Asked Questions

What can a knowledge graph do that a relational database cannot?
Multi-level relationship queries. "Find all customers whose suppliers also supply our competitors" requires nested JOINs in SQL and becomes impractical across many levels. In a knowledge graph, this is a simple traversal — fast and intuitive.
How much effort does building a knowledge graph require?
An initial knowledge graph with the most important entities and relationships is ready in three to four weeks. The biggest challenge is not technology but data modeling — which entities and relationships are business-relevant? We develop the model together with your domain experts.
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