Why Neo4j for
Knowledge Graphs

What is a 
Knowledge Graph?

A knowledge graph is a design pattern for storing, organizing, and accessing interrelated data entities, including their semantic relationships. With knowledge graphs, you can better understand your data and build more intelligent applications. 


Build With Knowledge Graphs

Represent structured, semi-structured, and unstructured data and their relationships naturally using nodes, relationships, and properties. Eliminate the translation from conceptual to physical models.

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Introduce new data, properties, and relationships without rebuilding the database or modifying application code.

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Write concise, flexible queries with fewer lines of code, tapping into powerful features like quantified path patterns for efficient graph traversal and pattern matching.

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Enrich your knowledge graphs with 65+ production-ready algorithms – including node embeddings, similarity metrics, and community detection – to power intelligent apps and improve decisions.

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Achieve up to 1000x faster queries versus relational and non-native graph databases. Neo4j’s native graph architecture eliminates complex joins and optimizes execution for rapid results.

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Integrate knowledge graphs seamlessly into your existing data ecosystem. Build on Neo4j with native drivers, connectors, and no-code import tools.

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The Buzz is Real

“The synergy between knowledge graphs and generative AI serves as a game-changer for businesses, driving transformative impacts across various organizational functions.”

“Knowledge graphs provide the perfect complement to LLM-based solutions where high thresholds of accuracy and correctness need to be attained.”

“Using an LLM-generated knowledge graph … vastly improves the retrieval portion of RAG, populating the context window with higher relevance content, resulting in better answers and capturing evidence provenance.”

Tools and Guides for Building With Knowledge Graphs