> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/zingg/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/zingg/running-zingg/knowledge-graph.md).

# Knowledge Graph

Build a knowledge graph on top of your resolved entities to analyze relationships across your data.

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Enterprise Plus only. Knowledge Graph is available in Zingg Enterprise Plus (ZinggES). Not available in Community, Enterprise Lite, or Enterprise.
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Entity resolution resolves who your entities are. The Knowledge Graph goes one step further - it maps the relationships between those entities once they are resolved.

After Zingg groups records into clusters and assigns Zingg IDs, the knowledge graph layer builds a graph structure on top of those resolved entities. This lets you query not just, "Is this the same customer?" but also "how are these customers connected to each other through shared addresses, accounts, devices, or transactions?"

Common applications include fraud detection (finding rings of connected entities), customer householding (understanding which individuals belong to the same household), supplier network analysis, and compliance screening (identifying indirect relationships between\
sanctioned entities and your counterparties).

### How it fits in the Zingg workflow

The Knowledge Graph is a post-match feature. The sequence is:

1. Run a match or incremental to resolve entities and assign Zingg IDs.
2. Zingg IDs become the nodes in the knowledge graph.
3. The knowledge graph layer maps edges between nodes based on shared attributes, transactions, or relationships defined by your data model.
4. Query the graph to find connected entities, paths between nodes, and clusters of related records.

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**Read more:**

* Entity resolution and graph analysis with Neo4j - [Connect Graph Databases](/zingg/connect-your-data/connect-graph-databases-neo4j.md)
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