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What is a knowledge graph?
A knowledge graph is a data structure that represents entities (people, products, places, concepts) and their relationships as a network. Instead of rows in tables: nodes and edges with semantic information. Example: 'Berlin' (node) 'is the capital of' (edge) 'Germany' (node).
Core components: entities (things, people, concepts), relationships (categorized connections—'works at,' 'is part of'), attributes (properties per node), ontology (schema with permitted types and relationships), rules (constraints and inference logic).
Well-known knowledge graphs: Google Knowledge Graph (behind Google Search), Wikidata (open, collaboratively maintained database), DBpedia (structured data from Wikipedia), enterprise knowledge graphs (internal corporate graphs at large companies), domain-specific graphs (medicine, chemistry, law).
Knowledge graphs are becoming increasingly interesting for small and medium-sized businesses—especially when combined with LLMs. Where structured knowledge is important (compliance, product relationships, customer networks), graphs provide significantly more precise answers than RAG alone. Setting them up is more complex, but the long-term benefits are significant.
Best Practices for Knowledge Graphs
These six principles have proven effective:
- Start small: One department, a few entity types—then expand.
- Buildthe ontology iteratively: Don’t plan for perfection—learn and adapt through practice.
- Ensure data quality: Incorrect facts in the graph propagate—build in quality assurance processes.
- Combine with LLMs: Graph-RAG is significantly more powerful than pure RAG or a pure graph.
- Early governance: Regulate maintenance, approvals, and changes in a structured way.
- Demonstrate business value: Show concrete applications, not just diagrams.
Common Mistakes in Knowledge Graphs
We often see these pitfalls:
- An overly ambitious ontology: Trying to model everything perfectly—the project gets bogged down in details.
- No business case: Graph built, but no one uses it—an expensive art installation.
- Data quality ignored: Automatically extracted facts without QA—the graph quickly becomes error-prone.
- No maintenance process: The graph becomes outdated—irrelevant after just a few months.
- No LLM integration: A graph on its own without AI applications—wastes potential.
Knowledge Graph vs. RAG vs. Database
A comparison of three knowledge structures:
- Knowledge graph: Interconnected facts with semantics. Ideal for reasoning and explainability.
- RAG: Similarity-based text search. Quick to set up, but lacks true structure.
- Database: Tables with fixed fields. For transactions—not for knowledge networks.
Build Knowledge Graphs with prodot
In a free initial consultation, we’ll identify your most important knowledge network and outline a knowledge graph pilot—complete with a clear business case.
As an AI partner for small and medium-sized businesses, we build knowledge graphs in a practical way—using ontology, data integration, graph databases, and LLM integration.
What We Offer
- AI Consulting — Knowledge Graph Design and Implementation.
- RAG in the Glossary — the related retrieval technique.
- Data governance —the foundation for clean graphs.
- Semantic search — a complementary technology.
