What is Knowledge Graph?
Turkish: Bilgi Grafiği (Knowledge Graph)
A knowledge graph models entities such as people, products, documents, and processes with relationships that systems can query.
What is Knowledge Graph?
A knowledge graph represents data as a network of entities and relationships rather than only as tables. Customers, products, partners, contracts, invoices, or employees can be nodes; relationships such as “purchased”, “belongs to”, or “is responsible for” connect them.
This structure is not only for storing records. It lets systems ask questions through relationships. For example, “customers who bought from the same supplier and opened a support ticket in the last 6 months” can be expressed naturally in a graph.
How It Works
Knowledge graphs are usually modeled with nodes, edges, properties, and a schema. GraphDB and similar graph databases make traversal across relationships efficient.
In AI applications, a knowledge graph can enrich RAG sources. Semantic search finds document passages, while the graph explicitly shows how entities are connected.
Business Use
Knowledge graphs are used for customer 360 views, product compatibility, risk analysis, fraud detection, technical asset inventory, and enterprise knowledge management. They are especially useful when data scattered across different systems must be connected through meaningful relationships.
Successful use depends on managing entity names, canonical records, and relationship types carefully. Otherwise the graph grows, but reliable querying becomes difficult.
Related Terms
A graph database models entities as nodes and edges, making relationship-heavy queries fast and natural in NoSQL systems.
RAG (Retrieval-Augmented Generation)RAG is an AI architecture where a language model retrieves relevant passages from documents or databases before generating an answer.
Semantic SearchSemantic search finds relevant results by comparing the meaning of queries and content, not only matching exact keywords.