Enhancing Retrieval with Structured Knowledge
SuNaAI Lab
Technical Guide Series
Discover how knowledge graphs transform traditional RAG into powerful, accurate question-answering systems
Traditional Retrieval-Augmented Generation (RAG) has revolutionized how we build AI systems that can answer questions using external knowledge. But standard RAG has limitations—it relies on semantic similarity alone, which can miss important relationships and context.
Knowledge Graph Augmented RAG combines the best of both worlds: the flexibility of vector embeddings and the precision of structured knowledge. By integrating knowledge graphs into your RAG pipeline, you can dramatically improve retrieval accuracy and answer quality.
Companies using KG-Augmented RAG report 30-50% improvements in answer accuracy, especially for questions requiring multi-hop reasoning and factual verification.
How knowledge graphs enhance traditional RAG systems
Knowledge Graph Augmented RAG is a hybrid approach that combines:
1. KNOWLEDGE GRAPH - Entities (nodes) - Relationships (edges) - Properties/attributes - Triplets: (subject, predicate, object) 2. VECTOR EMBEDDINGS - Dense representations - Semantic similarity - Document-level embeddings 3. HYBRID RETRIEVAL - Graph traversal for structured queries - Vector search for semantic similarity - Joint ranking and re-ranking 4. GENERATION - Context from KG + retrieved docs - Structured knowledge integration - Fact verification
Extract entities, relationships, and facts from your documents. Use NER (Named Entity Recognition) and relation extraction models to construct the graph automatically.
Combines vector search with graph traversal. For each query, retrieve both semantically similar documents (via embeddings) and related entities (via graph traversal).
Intelligently merge information from retrieved documents and knowledge graph paths to create a comprehensive context for generation.
LLM generates answers using both unstructured text and structured knowledge, enabling more accurate and factually consistent responses.
Query: "What research papers did the CEO of OpenAI publish?"
KG allows traversal: OpenAI → CEO → Person → Papers
Query: "How is company A related to company B?"
Directly query parent-child, partner, competitor relationships
Query: "Who was the CTO before the current one?"
Query temporal edges in the knowledge graph
Query: "Did Elon Musk found Tesla?"
Verify against structured graph facts
┌─────────────────────────────────────────────────────────┐
│ USER QUERY │
│ "Who founded Tesla and when?" │
└─────────────────┬───────────────────────────────────────┘
│
┌─────────┴──────────┐
│ │
▼ ▼
┌──────────────┐ ┌─────────────────┐
│ VECTOR │ │ KNOWLEDGE │
│ RETRIEVER │ │ GRAPH SEARCH │
│ │ │ │
│ • Embed │ │ • Entity Node │
│ query │ │ lookup │
│ • Semantic │ │ • Graph │
│ search │ │ traversal │
└──────┬───────┘ │ • Path │
│ │ expansion │
│ └────────┬────────┘
│ │
└──────────┬──────────┘
▼
┌────────────────────┐
│ CONTEXT FUSION │
│ │
│ • Merge docs │
│ • Add KG paths │
│ • Re-rank │
└──────────┬─────────┘
│
▼
┌────────────────────┐
│ LLM GENERATION │
│ │
│ • Generate with │
│ KG context │
│ • Fact-check │
└──────────┬─────────┘
│
▼
FINAL ANSWER# Extract entities and relations
from transformers import pipeline
ner = pipeline("ner", aggregation_strategy="simple")
re_extractor = pipeline("text-classification")
def build_kg_from_documents(docs):
kg = KnowledgeGraph()
for doc in docs:
# Extract entities
entities = ner(doc)
# Extract relations
relations = re_extractor(doc)
# Add to graph
for entity in entities:
kg.add_entity(entity)
for relation in relations:
kg.add_relation(relation)
return kgdef hybrid_retrieve(query, kg, vector_db):
# Vector retrieval
vector_results = vector_db.similarity_search(query, k=5)
# Extract entities from query
query_entities = extract_entities(query)
# Graph retrieval
kg_results = []
for entity in query_entities:
# Get related entities
neighbors = kg.get_neighbors(entity)
# Get documents mentioning these entities
docs = kg.get_documents_for_entities(neighbors)
kg_results.extend(docs)
# Combine and re-rank
combined = merge_results(vector_results, kg_results)
return re_rank(combined, query)def enrich_context_with_kg(context, query_entities, kg):
enriched_context = context
for entity in query_entities:
# Get KG facts about entity
facts = kg.get_facts(entity)
# Add structured facts to context
enriched_context += f"
Facts about {entity}:"
for fact in facts:
enriched_context += f"
{fact}"
return enriched_contextDon't over-rely on either. Use graph for structured queries, vector for semantic similarity. Combine both for best results.
As new documents arrive, incrementally update your knowledge graph. Use incremental indexing strategies to avoid full rebuilds.
Same entity names can refer to different things. Use entity linking and disambiguation techniques to handle ambiguity.
Consider Neo4j for complex queries, Qdrant/Dgraph for scalability. Match storage to your access patterns.