How to Combine Graph Search and Semantic Search

Graph search and semantic search solve different retrieval problems. Semantic search finds information by meaning. Graph search finds information by explicit relationships.

Combining them gives AI applications a stronger retrieval architecture: semantic search can find the right entry points even when the query is fuzzy, and graph search can expand from those entry points into connected entities, relationships, source chunks, and evidence.

Short Answer

To combine graph search and semantic search, use semantic search to find relevant entities, documents, or graph summaries, then use graph search to traverse relationships and collect connected context.

A common GraphRAG pattern is:

user query
  -> semantic search over entities or chunks
  -> map results to graph nodes
  -> traverse selected relationships
  -> retrieve source evidence
  -> rank and filter context
  -> pass context to the LLM

This works well when an AI system needs both meaning-based retrieval and relationship-aware context.

What Semantic Search Does Best

Semantic search retrieves content by meaning rather than exact wording.

It is useful when users ask questions in natural language, use synonyms, describe a concept indirectly, or do not know the exact name of an entity.

For example, a query like “systems affected by login failures” may retrieve content about authentication outages even if the source documents do not use the exact phrase.

What Graph Search Does Best

Graph search retrieves information by following explicit connections.

It is useful when the answer depends on relationships such as depends_on, owned_by, mentions, caused_by, applies_to, or supports.

For example, graph search can answer which applications depend on a service, which documents support a claim, or how two organizations are connected.

Why Combine Them?

Semantic search alone may find relevant chunks but miss connected context.

Graph search alone may require knowing the exact entity or relationship to start from.

Together, they solve both problems. Semantic search finds likely entry points. Graph search expands those entry points through known relationships.

Architecture Pattern 1: Semantic Search First, Graph Search Second

This is the most common pattern.

The system embeds the user query and searches over entity descriptions, document chunks, relationship summaries, or community summaries. The retrieved items are mapped to graph node IDs. Then graph traversal expands from those nodes.

This pattern works well when users ask fuzzy natural-language questions.

Architecture Pattern 2: Graph Search First, Semantic Search Second

Sometimes the application already knows the starting entity.

For example, a user may be viewing a customer profile, incident, product, or contract. The system can start with that known graph node, traverse related nodes, and then use semantic search to rank or filter connected documents.

This pattern works well for contextual assistants inside applications.

Architecture Pattern 3: Parallel Retrieval and Fusion

Another pattern runs graph search and semantic search in parallel.

Semantic search retrieves chunks or entities by meaning. Graph search retrieves connected facts and source evidence. The system then merges, deduplicates, ranks, and sends the best context to the LLM.

This is useful when both retrieval methods may independently find useful evidence.

Architecture Pattern 4: Entity-Centered GraphRAG

Entity-centered GraphRAG uses entities as the bridge between semantic search and graph traversal.

The system identifies entities in the query, finds matching graph nodes, retrieves neighbors, and collects source chunks linked to those entities.

This pattern is especially useful for domains with people, organizations, products, services, contracts, policies, events, or research concepts.

What to Embed

To combine semantic and graph search, decide which graph objects should have embeddings.

Useful embedding targets include:

source chunks
entity descriptions
relationship summaries
community summaries
document abstracts
graph neighborhood summaries

Each embedded object should keep a stable link back to graph IDs and source evidence.

What to Traverse

Graph traversal should be selective.

Do not follow every edge type for every query. Choose relationship types based on user intent.

For example, an impact-analysis query may traverse affects, depends_on, and owned_by. A compliance query may traverse applies_to, requires, and supported_by.

Example: Service Dependency Question

User query:

Which customer-facing systems may be affected by the login outage?

Combined retrieval flow:

semantic search finds the Authentication Outage entity and related incident notes
graph search traverses affects and depended_on_by relationships
the retriever collects affected services, applications, owners, and runbooks
source chunks are retrieved for evidence
the LLM answers with affected systems and citations

Example: Policy Question

User query:

Which data retention rules apply to European customer records?

Combined retrieval flow:

semantic search finds policy chunks about retention and European records
graph search maps those chunks to Policy, Region, and DataCategory nodes
traversal follows applies_to, governs, and references relationships
source evidence and policy summaries are ranked for the final answer

Ranking the Combined Results

Combining graph and semantic search creates more candidates than the LLM can use.

Rank results using signals such as:

semantic similarity score
path length from entry entity
relationship type importance
source confidence
recency
access permissions
number of supporting sources
deduplication across chunks and entities

The final context should be compact and evidence-rich.

Filters and Access Control

Filters should apply across both retrieval systems.

If semantic search retrieves a chunk the user cannot access, it should be excluded. If graph traversal crosses into restricted nodes or source documents, those results should also be excluded.

Access control must apply to vectors, graph nodes, edges, summaries, and source chunks.

Common Mistakes

Using semantic search and graph search as separate systems with no shared IDs.
Embedding graph objects but not linking them back to source evidence.
Traversing too many relationship types.
Expanding too many hops from weak semantic matches.
Sending graph labels to the LLM without source chunks.
Ignoring permissions during graph expansion.
Failing to rank or deduplicate combined results.

Best Practices

Use stable IDs between vector records and graph nodes.
Embed entity descriptions and source chunks first.
Use semantic search to find entry points for fuzzy queries.
Use graph search to expand connected context.
Attach evidence to every important graph fact.
Limit traversal depth and edge types.
Rank combined context before generation.
Evaluate against chunk-only RAG and graph-only retrieval.

When This Combination Helps

Combining graph and semantic search helps when questions are both fuzzy and relationship-heavy.

Good use cases include enterprise search, incident analysis, legal review, research synthesis, supply-chain intelligence, compliance, customer-account intelligence, healthcare, and software dependency analysis.

If questions are simple and contained in one paragraph, semantic search alone may be enough.

How to Evaluate It

Evaluate the combined system with real questions.

Measure whether it improves:

entity recall
relationship recall
source citation quality
answer faithfulness
coverage of multi-hop questions
latency
permission safety

The combination is worth using only when it improves retrieval quality enough to justify the added complexity.

Summary

Graph search and semantic search are complementary.

Semantic search finds relevant content by meaning. Graph search follows explicit relationships to gather connected context.

For AI applications and GraphRAG, a strong pattern is to use semantic search to find graph entry points, graph traversal to expand context, and source evidence retrieval to ground the final answer.