Knowledge Graphs for AI Applications

Model entities, relationships, and rules to power RAG, reasoning, recommendations, and compliance.

Knowledge graphs (KGs) provide structured context and reasoning for AI systems. Good schemas, consistent ingest, and query patterns enable better retrieval, explainability, and governance.

Capture access/ACL in edges or node metadata

Extract from sources (DBs, docs, APIs); map to schema.

Clean/validate; deduplicate; entity resolution.

live demo · related simulation● LIVE

Combine graph paths with vector passages

Ensure citations and provenance

Reasoning & Compliance

Frequently asked questions

What is the purpose of using path queries in a knowledge graph?

Path queries, such as shortest path algorithms or k-hop neighborhood searches, are used to traverse relationships within a knowledge graph and identify connections between entities.

How do pattern matching languages like Cypher, Gremlin, and SPARQL relate to knowledge graphs?

Pattern matching languages allow users to define complex queries that search for specific patterns or relationships within the data stored in a knowledge graph.

What is the role of combining graph traversal with vector embeddings in AI applications?

Combining graph traversal techniques with vector embeddings enables more sophisticated candidate generation and reranking processes, leveraging both structural and semantic information within the knowledge graph.

How can access control lists (ACLs) be integrated into aggregations performed on a knowledge graph?

Aggregations can incorporate ACL filters to ensure that results are filtered based on user permissions and data security policies, providing granular control over the information accessed.

▶ Try it live

Everything above runs in your browser — open Force-Directed Graph and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Force-Directed Graph simulation

Knowledge Graphs for AI Applications | ML Knowledge Hub