Agent Engineering Digest KB

Knowledge Graph Query Patterns for AI Applications

Knowledge graph query patterns are reusable ways to retrieve connected facts, entities, relationships, source evidence, and summaries for AI applications.

In GraphRAG and AI search, the goal is not only to ask a graph database a question. The goal is to retrieve the right structured context for an LLM, agent, or search experience. That usually means combining graph traversal, semantic search, filters, provenance, and ranking.

Short Answer

The most useful knowledge graph query patterns for AI applications include entity lookup, neighborhood expansion, path finding, evidence retrieval, community summary retrieval, provenance lookup, filtered graph traversal, multi-hop reasoning, and hybrid vector-plus-graph search.

Each pattern answers a different retrieval need. Entity lookup finds the right node. Neighborhood expansion gathers connected context. Path finding explains how things are connected. Evidence retrieval grounds graph facts in source documents. Hybrid search uses vector search to find entry points and graph traversal to expand them.

Why Query Patterns Matter

A knowledge graph can contain many entities and relationships, but AI systems need focused context.

If a retriever returns too little, the LLM may miss the answer. If it returns too much, the prompt becomes noisy and expensive.

Query patterns help developers retrieve the right amount of connected context for a specific task.

Pattern 1: Entity Lookup

Entity lookup finds a graph node from a name, alias, ID, or semantic query.

Examples:

  • find the entity for Acme Corp
  • find a product by SKU
  • find a person by email
  • find the incident mentioned in the user query

This is often the first step in GraphRAG because entity nodes become entry points for traversal.

Pattern 2: Alias-Aware Lookup

Alias-aware lookup finds an entity even when the query uses a different name.

For example, IBM, IBM Corp., and International Business Machines may refer to one canonical entity.

This pattern depends on entity resolution and alias fields. Without it, graph retrieval can fragment across duplicate nodes.

Pattern 3: One-Hop Neighborhood Expansion

One-hop neighborhood expansion retrieves directly connected nodes.

For example:

Service A
  -> depends_on -> Database B
  -> owned_by -> Platform Team
  -> mentioned_in -> Runbook 7

This pattern is useful when the answer depends on immediate relationships around an entity.

Pattern 4: Multi-Hop Traversal

Multi-hop traversal follows relationships beyond direct neighbors.

For example:

Incident
  -> affects -> Service
  -> depended_on_by -> Application
  -> used_by -> Customer

This pattern is useful for dependency analysis, impact analysis, supply-chain questions, and organizational reasoning.

Use multi-hop traversal carefully. More hops can improve recall but also add noise.

Pattern 5: Path Finding

Path finding explains how two entities are connected.

Example questions:

  • How is this supplier connected to this risk?
  • How is this customer affected by this incident?
  • How is this paper related to this method?

Path queries are useful because they return the chain of relationships, not just a set of related nodes.

Pattern 6: Source Chunk Retrieval

Source chunk retrieval maps graph facts back to document evidence.

For example, if a graph contains Policy A applies_to Region B, the query should retrieve the chunk or document that supports that relationship.

This pattern is essential for grounded RAG because LLMs should receive evidence, not only graph labels.

Pattern 7: Provenance Lookup

Provenance lookup answers where a graph fact came from.

Useful provenance fields include source document ID, chunk ID, evidence text, extraction time, model version, and confidence.

This pattern helps with citations, audits, debugging, and trust.

Pattern 8: Relationship Summary Retrieval

Some graphs store summaries of relationships, especially when the same relationship appears across many documents.

Instead of returning every raw edge or every source chunk, the query can retrieve a relationship summary plus supporting evidence.

This is useful when the LLM needs a compact explanation of how two entities are connected.

Pattern 9: Community Summary Retrieval

Community summary retrieval returns summaries of graph clusters.

This helps answer broad questions such as:

  • What are the main themes in this research corpus?
  • What supplier risk clusters appear in these documents?
  • Which groups of incidents share similar causes?

Community summaries are useful when direct chunk retrieval would return too many fragments.

Pattern 10: Filtered Graph Traversal

Filtered traversal applies constraints while moving through the graph.

Filters may include:

  • tenant
  • user role
  • document visibility
  • date range
  • relationship type
  • entity type
  • confidence threshold
  • source system

Filtered traversal is critical for enterprise AI systems because not every connected fact should be visible to every user.

Pattern 11: Entity-to-Document Retrieval

This pattern starts from an entity and retrieves documents that mention or support it.

For example, a user may ask about a product, and the system retrieves policy documents, support tickets, release notes, and incidents connected to that product.

This works well when the graph stores DocumentChunk -- mentions -- Entity links.

Pattern 12: Document-to-Entity Retrieval

This pattern starts from a document or chunk and retrieves entities mentioned inside it.

It is useful when a vector search finds a relevant chunk first. The system can then ask: which graph entities are connected to this chunk?

From there, it can expand to related entities and supporting documents.

Pattern 13: Hybrid Vector and Graph Search

Hybrid vector and graph search uses semantic search to find entry points, then graph traversal to gather connected context.

A common flow is:

query
  -> vector search over entity summaries
  -> retrieve top entity IDs
  -> traverse graph from those entities
  -> collect source chunks and summaries
  -> rank context for the LLM

This pattern combines semantic flexibility with relationship-aware retrieval.

Pattern 14: Question-Type Routing

Different questions need different query patterns.

Examples:

  • “What is X?” may use entity lookup and source chunk retrieval.
  • “How is X related to Y?” may use path finding.
  • “What is affected by X?” may use multi-hop traversal.
  • “What are the main themes?” may use community summaries.
  • “Why do we believe this?” may use provenance lookup.

AI applications can route queries to the pattern that fits the user intent.

Pattern 15: Evidence-First Answering

Evidence-first answering retrieves source evidence before asking the LLM to respond.

The graph may identify relevant entities and relationships, but the final context should include source chunks, citations, or evidence text.

This reduces the risk that the LLM answers from graph labels without grounding.

Common Mistakes

  • Using one generic graph query for every user question.
  • Expanding too many hops without ranking.
  • Returning graph nodes without source evidence.
  • Ignoring aliases and entity resolution.
  • Letting high-degree generic nodes dominate results.
  • Forgetting access-control filters during traversal.
  • Using vector search only and never traversing relationships.

Best Practices

  • Start with the user question type.
  • Use entity lookup for specific named things.
  • Use path finding for connection questions.
  • Use neighborhood expansion for local context.
  • Use community summaries for broad synthesis.
  • Always preserve source evidence.
  • Limit traversal depth and rank expanded context.
  • Combine vector search and graph traversal when the query is fuzzy.

How to Evaluate Query Patterns

Evaluate each pattern with real questions.

Useful checks include:

  • Did the query find the right entity?
  • Did it retrieve the necessary relationships?
  • Did it include source evidence?
  • Did it avoid irrelevant neighbors?
  • Did it respect access controls?
  • Did the final answer improve over chunk-only RAG?
  • Was latency acceptable?

Summary

Knowledge graph query patterns give AI applications reusable ways to retrieve connected context.

The most important patterns include entity lookup, alias-aware lookup, neighborhood expansion, multi-hop traversal, path finding, source chunk retrieval, provenance lookup, community summaries, filtered traversal, and hybrid vector-plus-graph search.

For GraphRAG, the strongest systems do not rely on one query shape. They route each user question to the graph pattern that best matches the retrieval need.