Knowledge graph
A knowledge graph is a structured form of representing knowledge consisting of entities (nodes) and their relationships (edges). It represents real knowledge in machine-readable form and links information semantically. Knowledge graphs improve search results, enable semantic links and serve as a reliable source of knowledge for AI systems, in RAG architectures for instance. They join the understanding of meaning to the traceability of structured data.
Also known as: knowledge graph, semantic network
How is a knowledge graph structured?
A knowledge graph organises knowledge as a network. The nodes stand for entities: things, people, places, concepts or events. The edges describe how those entities are connected, "Elisabit is an agency in Germany" or "product X belongs to category Y" for instance. Every entity and every relationship can additionally carry properties (attributes), so a detailed, multi-dimensional picture of reality emerges.
These connections are often represented as so-called triples: subject, predicate, object. Linking many such triples creates a dense, semantically rich network. Unlike a classic table, the graph makes relationships explicit and analysable by machine. Many knowledge graphs rest on established standards such as RDF and query languages such as SPARQL, or on property graph models, which have different strengths depending on the use case.
An essential part is often an ontology or schema defining which kinds of entities and relationships exist and how they stand to one another. This vocabulary ensures consistency and makes it possible to place new information without contradiction. A knowledge graph thus stays understandable and logically searchable even as complexity grows.
What are knowledge graphs used for?
One prominent example is the semantic search. Instead of merely matching keywords, search systems use a knowledge graph to understand a query’s context and return more precise results. Well-known search engines use knowledge graphs for the info boxes that appear beside the results, bringing together facts about people, places or organisations.
Recommendation systems, fraud detection and the integration of distributed data sources benefit from this structure too. In fraud detection, suspicious patterns can be recognised by analysing relationships between accounts, transactions and people, connections that would stay hidden in isolated tables. Recommendation systems in turn use the interconnection to suggest not only similar but thematically related content.
In business, knowledge graphs bring knowledge from different systems together and represent it consistently. The result is a central, connected Knowledge basethat different applications can access. They bridge classic data silos and create a shared, semantically unambiguous view of the most important business objects.
What role does the knowledge graph play for AI and RAG?
For modern AI systems, knowledge graphs are a valuable source of knowledge. In RAGarchitectures (retrieval-augmented generation), relevant facts are fetched from an external source and given to a Language model made available. A knowledge graph supplies structured, solid information and reduces the risk of hallucinations, because the model draws on verified knowledge rather than mere probabilities.
Compared with purely vector-based search, a knowledge graph offers the advantage of explicit relationships. Systems can thus find not only similar but logically linked information, indirect connections across several entities for instance. This ability to reason across several steps (multi-hop) is decisive for complex questions where the answer emerges from the combination of several facts.
Many modern solutions combine both approaches in so-called graph RAG architectures. Vector search finds thematically fitting passages, while the knowledge graph contributes precise facts and their relationships. The result is a system that responds flexibly to natural language and gives traceable, factually accurate answers.
How does a knowledge graph differ from a database?
A classic relational database stores data in tables with fixed columns. Relationships there have to be brought together laboriously through joins, which quickly becomes complex and slow with many nested relationships. A knowledge graph, by contrast, represents relationships directly as edges, which considerably eases and speeds up complex, interconnected queries.
The graph therefore suits use cases in which the connections between data points matter as much as the data points themselves. It can also be extended flexibly: new entity types and relationships can be added without rebuilding a rigid table schema. That makes knowledge graphs particularly adaptable in environments where knowledge changes continuously.
How do you build a knowledge graph successfully?
Building one begins with a clear question: which business questions is the graph to answer? The relevant entities, relationships and data sources follow from that. Information is then extracted from structured and unstructured sources, cleaned and, an often underestimated step, linked reliably (entity resolution), so the same real entity is not captured more than once.
Decisive for success are a well-considered ontology and continuing maintenance, since a knowledge graph is not a one-off project but a living system. Anyone wanting to represent knowledge flexibly, semantically and extensibly finds a fitting tool in the knowledge graph. Elisabit helps companies build such knowledge structures for search, content and AI sensibly and integrate them lastingly into existing processes.
Frequently asked questions
What is a knowledge graph, simply explained?
A knowledge graph is a web of knowledge in which things (entities) are shown as nodes and their relationships as edges. Knowledge is thus represented in a structured, machine-readable, semantically linked way, for search and AI for instance.
What is a knowledge graph used for?
Typical uses are semantic search, recommendation systems, data integration and knowledge management. In AI it serves as a reliable knowledge source, for instance in RAG systems, to ground answers in fact.
What is the difference between a knowledge graph and a vector database?
A Vector database finds similar information through numerical representations. A knowledge graph, by contrast, makes explicit relationships visible and allows logical links across several entities. The two approaches are often combined.
Does a knowledge graph help against hallucinations?
Yes, indirectly. By having a language model draw on structured, verified facts from a knowledge graph, the risk of invented answers can be reduced. The graph supplies solid ground rather than mere probabilities.
How much work is building a knowledge graph?
The effort depends on data quality, the variety of sources and the objective. A clear ontology, reliable linking of identical entities and continuous maintenance matter. A knowledge graph is a living system, not a one-off project.
Related terms
RAG combines a language model with the retrieval of relevant information from external knowledge sources before answering.
Search by meaning rather than exact keywords — based on embeddings and vector search.
A database that stores content as embeddings and enables fast similarity search for AI applications.
A structured, maintained body of knowledge that serves as a source for AI applications, RAG and customer service.
An AI hallucination is output from an AI model that sounds plausible but is factually wrong.
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