What Are Knowledge Graphs?

We hear the term knowledge graph everywhere now — from Google Search to enterprise AI to GenAI apps. But what exactly is a knowledge graph, and why is everyone suddenly obsessed with it?

In this post, I’ll break down knowledge graphs in plain language: what they are, how they work, and how I use them in my own projects.

🧱 The Basics: What Is a Knowledge Graph?

At its core, a knowledge graph is a network of real-world entities (people, places, things) and the relationships between them. It’s how machines can represent, understand, and reason about the world — kind of like a human brain, but for structured data.

In technical terms:

A knowledge graph is a graph-based data structure that encodes entities as nodes and relationships as edges, enriched with semantics via an ontology.

Here’s a simple example:

“Elon Musk” —[CEO of]→ “Tesla” “Tesla” —[makes]→ “Cybertruck” “Cybertruck” —[type]→ “Electric Vehicle”

Every node is an entity. Every edge is a predicate (relationship). And the whole structure is queryable, explorable, and often inferable.

🕸️ Why Use a Graph?

Traditional databases work with rows and tables. But in real life, information is messy and connected.

• A person can work for multiple companies.
• A product can belong to many categories.
• Concepts can be linked across domains.

Graphs handle this interconnectedness naturally. That’s why big tech uses them:

• Google’s Knowledge Graph for better search answers.
• Facebook’s social graph to model user relationships.
• Amazon’s product graph for recommendations.

💡 How Is It Different from a Database?

Graphs are schema-light and flexible, which makes them perfect for AI, NLP, and dynamic domains.

🧠 Where Are Knowledge Graphs Used?

Here’s where knowledge graphs really shine:

• 🔍 Semantic Search
  Enables intent-based results (e.g., “founder of Tesla” → Elon Musk)

• 🧠 LLM Context Injection (RAG)
  Use a knowledge graph to retrieve precise facts and inject them into prompts — improving GenAI accuracy.

• 🏥 Healthcare & Life Sciences
  Model relationships between diseases, symptoms, genes, drugs.

• 💼 Enterprise Intelligence
  Unify data silos across CRM, HR, finance, support.

• 🔗 Data Integration & Interoperability
  Link structured + unstructured data through common semantics.

⚙️ Key Components of a Knowledge Graph

Here’s what goes into a real-world knowledge graph:

1. Entities: The nodes — people, places, products, concepts.
2. Relationships: The edges — how entities are connected.
3. Ontology: Defines classes, properties, and constraints. (Read my ontology post for details!)
4. Identifiers: Unique URIs to refer to each concept.
5. Query Layer: Languages like SPARQL or Cypher to retrieve insights.

🧪 My Use Cases

I’ve worked with knowledge graphs in several domains:

• Insurance Claims Automation: Extract structured facts from documents using OpenAI + Neo4j to speed up FNOL (First Notice of Loss).
• RAG Pipelines: Create mini knowledge graphs from PDFs and inject triples into prompts for better LLM accuracy.
• German Tax Assistant: Model deductions, expenses, and income types as nodes to generate explainable tax advice.

Whether it’s documents, chatbots, or graphs powering LLMs — KGs make AI smarter and explainable.

🧰 Popular Tools for Building KGs

• Neo4j: Popular graph database (uses Cypher).
• RDF & SPARQL: W3C standards for linked data.
• Stardog: Enterprise-grade knowledge graph platform.
• GraphDB: Great for RDF-based graphs.
• LangChain: Integrates LLMs with KG-based retrievers.

🔍 Final Thoughts

If you’re working with LLMs, messy data, or just want your system to understand things better, knowledge graphs are a superpower. They’re the connective tissue between raw data and semantic meaning.

In a world where AI often hallucinates, knowledge graphs ground your models in truth, logic, and explainability.

Got a use case you’re working on? Feel free to reach out — happy to jam on graph ideas!
— Akshat