Graph Reasoning

Graph Reasoning: Knowledge Graph (KG) vs. GraphRAG # 

Knowledge Graph (Structure) ➕ LLM (Reasoning) ➡️  GraphRAG (Hybrid QA System)

🔄 Relationship Between KG and GraphRAG #

  • A Knowledge Graph can serve as the data backbone for GraphRAG.
  • KG provides the structured facts, GraphRAG adds flexible retrieval and LLM reasoning.
  • GraphRAG extracts subgraphs, embeds them, and uses LLMs to generate natural language answers.
  • LLMs can also help enrich the KG, creating a feedback loop.
  • GraphRAG is essentially a modern hybrid system, combining symbolic structure with neural flexibility.

Side-by-Side Comparison #

Feature KG GraphRAG
🧩 Core Idea Graph-based representation of entities and relationships Retrieval-Augmented Generation with a graph-structured retrieval backend
🏗️ Structure Nodes (entities/concepts) + edges (relations) Combines a graph + retriever + LLM (generator)
🔍 Primary Use Case Semantic search, reasoning, data integration, and explainable AI Answering complex queries with structured reasoning + natural language generation
🧠 Reasoning Type Symbolic / rule-based / graph traversal Hybrid: retrieval + neural reasoning over graph paths
🧮 Tech Stack RDF, OWL, Neo4j, Blazegraph, SPARQL LangChain, LlamaIndex, HuggingFace Transformers, Neo4j/NetworkX for graph, vector DBs
🧪 Inference Deterministic (SPARQL, rules, logic) or probabilistic (PGMs) LLM-based generation informed by graph-aware retrieval
🔗 Integration with LLMs Optional; LLMs can query or summarize KG Essential; LLMs decode retrieved graph info into answers
📘 Example in Healthcare “What drugs interact with Warfarin?” → Uses drug-drug interaction KG “What treatments are best for elderly diabetic patients with hypertension?” → Uses patient-condition-treatment graph to guide LLM
📈 Scalability Can grow large but needs curation and consistency Scalable via modular retrievers; dynamic context injection
📣 Explainability High: paths are interpretable Medium: explainable only if LLM is instructed to reason with trace
📚 Data Format Triples: (subject, predicate, object) Graph-augmented documents, vector embeddings, node-context pairs
🎯 Strengths Precision, transparency, semantic integrity Flexibility, context-aware QA, natural language synthesis
🧱 Weaknesses Hard to build/maintain at scale, brittle for unstructured text Less structured, can hallucinate, graph reasoning quality depends on retriever design

🩺 Healthcare Use Cases #

Use Case KG GraphRAG
1. Drug Interaction Checks KG connects drugs via known interaction relationships from structured databases (e.g., RxNorm, DrugBank).
🔹 “Does Warfarin interact with NSAIDs?” → Traverse KG paths.		 GraphRAG retrieves documents discussing drug interactions, side effects, or contraindications and summarizes them.
🔹 “What should patients taking Warfarin avoid?”
2. Clinical Decision Support Encodes clinical guidelines as rules and semantic paths (e.g., “If diabetic AND hypertensive THEN consider ACE inhibitors”). Retrieves relevant chunks of guidelines and case studies, then LLM synthesizes a tailored answer.
🔹 “Best treatment plans for elderly diabetic patients with kidney disease?”
3. Patient Phenotyping Uses ontologies (e.g., SNOMED CT, HPO) to infer phenotypes based on coded EHR data.
🔹 Identify “Type 2 Diabetes” from a network of symptoms and lab values.		 Retrieves semantically similar patient trajectories or phenotypes, helping answer:
🔹 “How was this phenotype managed in similar patients?”
4. Rare Disease Diagnosis Graph-based inference across symptoms, genes, and conditions to suggest candidate diseases. Combines graph paths with medical literature to support LLM-based diagnostics and explanations.
🔹 “What rare diseases match these symptoms?”
5. Biomedical Research Discovery Connects genes, diseases, pathways, and drugs to suggest new hypotheses.
🔹 “Which genes are linked to both Parkinson’s and depression?”		 Retrieves multi-hop literature paths and generates natural language hypotheses.
🔹 “What is the link between gut microbiome and Alzheimer’s?”
6. Clinical Trial Matching Links patient features to trial eligibility criteria through structured relationships. Matches unstructured patient notes with trials via hybrid graph + text retrieval.
🔹 “Which clinical trials is this patient eligible for?”
7. Medical Education / Q&A Students query a structured KG to explore medical knowledge interactively. Natural language Q&A system over combined textbook + graph data.
🔹 “Explain why beta-blockers are contraindicated in asthma patients.”