Blogs

Understanding Ontology, Knowledge Graphs, RAG, and GraphRAG in LLM Applications

Posted by Jesse JCharis

Feb. 25, 2025, 12:51 a.m.

Understanding Ontology, Knowledge Graphs, RAG, and GraphRAG in LLM Applications

Introduction

In the realm of artificial intelligence (AI), structured knowledge representation and retrieval techniques are pivotal for enhancing Large Language Models (LLMs). This article clarifies four key concepts—Ontology, Knowledge Graphs, Retrieval-Augmented Generation (RAG), and GraphRAG—highlighting their differences and practical applications in LLM-driven systems.

1. Definitions

Ontology

An ontology is a formal framework that defines concepts, relationships, rules, and constraints within a specific domain. It acts as a schema for organizing knowledge hierarchically and semantically.
Example:
A medical ontology defines classes like Disease, Symptom, and Treatment, with relationships such as Disease hasSymptom Symptom.

Knowledge Graph

A knowledge graph is a structured database representing real-world entities (nodes) and their relationships (edges). It operationalizes ontologies by populating them with concrete data instances.
Example:
Google’s Knowledge Graph links Leonardo da Vinci → created → Mona Lisa, using data from diverse sources.

Retrieval-Augmented Generation (RAG)

RAG enhances LLMs by retrieving relevant information from external sources (e.g., documents or databases) before generating responses. This reduces hallucinations and improves accuracy.
Example:
A chatbot retrieves product manuals to answer technical queries accurately.

GraphRAG

GraphRAG integrates knowledge graphs into RAG systems. Instead of searching unstructured text, it traverses graph structures to retrieve contextually rich relationships for LLM responses.
Example:
A legal assistant uses case law graphs to identify precedent connections when drafting arguments.

2. Key Differences

3. Practical Applications

(a) Ontology in Healthcare Diagnosis

A hospital uses an oncology ontology to standardize cancer classifications:

• Classes: Tumor, Biomarker, Drug
• Relationships: Tumor expresses Biomarker, Drug targets Biomarker

LLM Application:
An AI system cross-references patient data against this ontology to recommend personalized treatments based on biomarker matches.

(b) Knowledge Graph for E-Commerce Recommendations

An online retailer builds a knowledge graph linking:

• Users → Purchased → Products
• Products → SimilarTo → OtherProducts

LLM Application:
When a user asks, “Find me shoes like my last Nike pair,” the system traverses the graph to suggest alternatives based on material or brand affinity.

(c) RAG for Customer Support

A telecom company deploys a RAG-powered chatbot that:

1. Retrieves FAQs from internal documents using semantic search.
2. Generates answers like:
   “Your router issue may require a firmware update—see [Step-by-Step Guide].”

(d) GraphRAG for Legal Research

A law firm implements GraphRAG with a case law knowledge graph: 1 Nodes: Cases, Judges, Statutes
2 Edges: Case cites Statute, Judge presidedOver Case

When asked about “Fourth Amendment privacy rulings,” the system retrieves connected cases like Katz v. U.S. and generates summaries with precedent analysis.

4. Comparative Analysis

5. Challenges & Solutions

1. Ontology Development:

   • Challenge: Time-intensive schema design (~6 months for complex domains).
   • Solution: Auto-generate ontologies using tools like Diffbot or Owlready2.

2. Knowledge Graph Scalability:

   • Challenge: Managing billion-scale nodes efficiently.
   • Solution: Use distributed graph databases like Amazon Neptune.

3. RAG Precision:

   • Challenge: Irrelevant document retrieval due to keyword mismatch.
   • Solution: Hybrid search combining vector + keyword methods.

4. GraphRAG Complexity:

   • Challenge：High computational overhead for real-time traversal．
   • Solution：Precompute subgraphs or use approximate nearest-neighbor algorithms．

6 Future Trends

1．Auto-Generated Ontologies: NLP tools will create domain-specific ontologies from unstructured text．
2．Federated Knowledge Graphs: Cross-organizational graphs enabling secure data sharing（e.g., healthcare networks）．
3．Graph-Aware LLMs: Native integration of graph traversal into transformer architectures．

Conclusion

While ontologies provide the foundational schema for organizing knowledge，knowledge graphs operationalize this structure．Traditional RＡＧ enhances LLMs with external data，and GraphＲＡＧ elevates this by leveraging interconnected graph insights．Together，they enable precise，context-rich AI applications across industries．Organizations adopting these technologies can expect improved accuracy，scalability，and reasoning capabilities in their AI systems．