[EAAPL-RAG009] Graph Retrieval-Augmented Generation

Category: Artificial Intelligence / Retrieval-Augmented Generation Sub-category: Knowledge Graph and Structured Retrieval Version: 1.1 Maturity: Emerging Tags: rag knowledge-graph graph-traversal entity-linking neo4j sparql cypher explainability multi-hop Regulatory Relevance: EU AI Act Article 13 (Explainability via graph reasoning paths), ISO/IEC 42001 Section 8.4, APRA CPG 234 (explainable AI in regulated decisions)

1. Executive Summary

Graph RAG combines vector-based semantic retrieval with structured knowledge graph traversal to answer complex, relationship-heavy questions with explainable reasoning paths. Where standard vector RAG retrieves semantically similar text passages, Graph RAG traverses explicit entity relationships to assemble contextual evidence — enabling answers to questions like "Which clinical trials reference the same molecular pathway as Drug X, and which of our researchers have published on that pathway?" that require following chains of typed relationships across a knowledge graph.

For enterprise leaders, the key differentiator of Graph RAG is not just retrieval breadth but explainability: every answer can be traced through a graph traversal path that names the entities and relationships used, providing the kind of structured reasoning chain that regulators, auditors, and domain experts can inspect and validate. In regulated industries where AI decisions must be explainable, a graph reasoning path ("Drug X → inhibits → Pathway Y → implicated_in → Disease Z → treated_by → Drug A") is far more auditable than "these five text passages were retrieved and the LLM generated this answer." Graph RAG represents the frontier of enterprise AI knowledge infrastructure — requiring significant investment in knowledge graph construction and maintenance, but delivering retrieval quality and explainability that vector-only approaches cannot match.

2. Problem Statement

Business Problem

Multi-hop relational queries — questions that require following chains of relationships between entities — cannot be answered by vector similarity search because the relevant documents may not be semantically similar to the query; they are structurally connected through a chain of relationships. An insurance underwriter asking "Which of our policyholders have risk factors associated with the newly classified Category 3 flood zone announced this week?" needs entity-level reasoning: flood zone classification → properties in zone → policies covering those properties → policyholders — a chain of four typed relationships across four entity types.

Technical Problem

Vector embeddings encode semantic similarity, not relational structure. A vector search cannot traverse relationships. Graph databases excel at relationship traversal but cannot perform semantic similarity search. Neither approach in isolation can answer relationship-heavy questions efficiently. A hybrid architecture that applies each approach to what it does best — semantic retrieval for passage-level context, graph traversal for relationship-level reasoning — is required.

Symptoms

• RAG system cannot answer questions about relationships between entities even though those relationships are documented in the corpus
• Users who need to trace reasoning paths (auditors, regulators, compliance officers) cannot follow the basis of an AI-generated answer
• Complex multi-entity queries require users to manually construct the relationship chain across multiple searches
• Standard RAG retrieves many topically relevant but structurally irrelevant documents, forcing the LLM to reason about relationships that should have been traversed at the retrieval layer

Cost of Inaction

• Inability to automate complex relational knowledge work (supply chain analysis, clinical trial matching, regulatory impact analysis)
• Auditors and regulators cannot accept AI-generated answers without explainable reasoning paths — Graph RAG is often the only approach that satisfies explainability requirements
• Missed enterprise intelligence: relationships between entities across the knowledge corpus remain unqueryable by AI

3. Context

When to Apply

• Knowledge domains with rich, well-defined entity relationships (biomedical, legal, financial, supply chain)
• Use cases requiring explainable reasoning paths as a regulatory or governance requirement
• Multi-hop queries where the answer requires traversing 2–5 entity relationship hops
• Organisations with existing knowledge graph infrastructure (Neo4j, Amazon Neptune, Azure Cosmos DB Gremlin) that can be extended for RAG
• Domains where entity-level precision is more important than document-level recall

When NOT to Apply

• Knowledge domains with no significant entity relationship structure (narrative documents without named entities)
• Organisations without the data engineering capacity to build and maintain a knowledge graph
• Queries that are purely semantic similarity-based with no multi-hop relational structure
• Prototyping phases — knowledge graph construction is expensive; standard RAG should be deployed first and Graph RAG added incrementally

Prerequisites

• An entity extraction and relationship extraction NLP pipeline (or a curated knowledge graph)
• A graph database (Neo4j, Amazon Neptune, Azure Cosmos DB for Apache Gremlin)
• A graph-to-text context generation component
• An ontology or schema defining the entity types and relationship types in the domain
• Entity linking capability to connect documents and text mentions to graph entities

Industry Applicability

4. Architecture Overview

Graph RAG is architecturally more complex than standard vector RAG because it requires maintaining two complementary knowledge representations — a vector index for semantic retrieval and a knowledge graph for relational traversal — and an orchestration layer that decides when to use each.

Knowledge Graph Construction

The knowledge graph is built through an extraction pipeline that processes the document corpus to identify entities and the relationships between them. The extraction pipeline applies:

1. Named Entity Recognition (NER): identify entities of defined types (Person, Organisation, Drug, Regulation, etc.) in each document
2. Coreference Resolution: link pronoun and noun phrase references to the canonical entity they refer to
3. Relationship Extraction: identify typed relationships between entity mentions (e.g., "Company A acquired Company B" → ACQUIRED(A, B))
4. Entity Linking: map extracted entity mentions to canonical graph nodes (e.g., "APRA" and "Australian Prudential Regulation Authority" → same node)
5. Graph Population: write canonical nodes and relationship edges to the graph database with source document provenance

Knowledge graph construction can be automated (using LLM-based extraction — GPT-4, Claude, or specialised models like REBEL) or semi-automated (LLM extraction with human review). For regulated domains (medical, legal), human domain expert review of extracted relationships is strongly recommended before ingestion.

Hybrid Query Routing

When a query arrives, an intent classifier determines whether the query is primarily:

• Semantic: "Explain the concept of operational risk" → route to vector retrieval
• Relational: "Which entities are connected to X via relationship type Y?" → route to graph traversal
• Hybrid: "What does the policy say about entities connected to X?" → parallel vector + graph

For hybrid queries, graph traversal retrieves the relevant entity subgraph (nodes and edges within N hops of the query entity), and this subgraph is serialised as text context and combined with vector-retrieved passages in the context window.

Graph Traversal for Context Assembly

Given a query entity (identified via entity linking), the graph traversal executes a configurable-depth Cypher (Neo4j) or SPARQL query to retrieve the entity neighbourhood: adjacent entities and their relationship types within a specified hop count. For example: MATCH (e:Drug {name: 'Drug X'})-[r1]->(n1)-[r2]->(n2) RETURN e, r1, n1, r2, n2 retrieves all 2-hop neighbours of Drug X with their relationship types.

The retrieved subgraph is serialised into a structured text representation for the LLM context window: "Drug X INHIBITS Enzyme A; Enzyme A INVOLVED_IN Pathway B; Pathway B IMPLICATED_IN Disease Y." This serialisation preserves the relational structure in a form the LLM can reason over, while the graph traversal path constitutes an explicit reasoning chain for explainability.

Combining Graph and Vector Context

The context assembler combines graph-derived context (serialised relationship chains) with vector-retrieved text passages. Graph context provides the relational structure; vector context provides the textual detail. The LLM is prompted to use both and to cite both — graph path citations take the form "[via: Drug X → INHIBITS → Enzyme A → INVOLVED_IN → Pathway B]".

5. Architecture Diagram

6. Components

7. Data Flow

Primary Flow

Error Flow

8. Security Considerations

Graph Database Access Control

The graph database must enforce node-level and edge-level access controls. In enterprise deployments, not all employees should be able to traverse all entity relationships (e.g., individual employee salary nodes should not be traversable by peers). Graph database access control is less mature than relational database RBAC — evaluate the access control capabilities of your chosen graph database (Neo4j roles, Neptune IAM, Cosmos Gremlin RBAC) carefully.

OWASP LLM Top 10 Mitigations

9. Governance Considerations

Knowledge Graph Quality Governance

The knowledge graph is a critical data asset. Every node and edge must have a provenance record (which document it was extracted from, by which model, and when). Incorrect relationships in the graph can produce systematically incorrect AI answers across all queries that traverse those relationships — a much higher risk than a single incorrect text chunk. Domain expert review of extracted relationships before graph population is strongly recommended for regulated domains.

Graph Schema (Ontology) Governance

The graph schema (entity types, relationship types, cardinality constraints) must be version-controlled and subject to a formal change management process. Schema changes may require partial or full re-extraction of the document corpus.

Governance Artefacts

10. Operational Considerations

Monitoring

Service Level Objectives

11. Cost Considerations

Cost Drivers

Indicative Cost Range

12. Trade-Off Analysis

Graph Database Selection

Graph Construction Approach

Architectural Tensions

13. Failure Modes

14. Regulatory Considerations

15. Reference Implementations

AWS

• Graph DB: Amazon Neptune (Gremlin or SPARQL)
• Extraction: SageMaker + custom NER model; Bedrock for LLM-based relationship extraction
• Vector index: OpenSearch k-NN
• Orchestration: Lambda + Step Functions; LangChain graph RAG

Azure

• Graph DB: Azure Cosmos DB for Apache Gremlin, or Neo4j on Azure Marketplace
• Extraction: Azure AI Language (NER) + Azure OpenAI for relationship extraction
• Vector index: Azure AI Search
• Orchestration: Prompt Flow with graph retriever

Self-Hosted

• Graph DB: Neo4j Community or Enterprise on Kubernetes
• Extraction: spaCy + REBEL (open-source relationship extraction) + Ollama for LLM extraction
• Vector index: Weaviate or Qdrant
• Orchestration: LangChain GraphCypherQAChain or LlamaIndex KnowledgeGraphIndex

16. Related Patterns

17. Maturity Assessment

Overall Maturity: Emerging — Knowledge graph construction tooling is mature (Neo4j, Neptune); LLM-based relationship extraction is proven; end-to-end Graph RAG pipelines are in production at a small number of leading enterprises; the pattern requires significant engineering investment and is not yet commoditised.

18. Revision History