Knowledge Graphs vs Property Graphs: Choosing the Model
Understand the two titans of the graph world. Learn the difference between RDF Knowledge Graphs and Labelled Property Graphs, and why Property Graphs are the preferred choice for modern RAG systems.
Knowledge Graphs vs Property Graphs: Choosing the Model
Throughout this module, we have used the terms "Knowledge Graph" and "Property Graph" somewhat interchangeably. However, in the professional world of data engineering, these are two distinct "Families" of graph modeling. Choosing the right one determines your query language, your database performance, and how your LLM interacts with the data.
In this final lesson of Module 4, we will compare the Resource Description Framework (RDF)—the foundation of the Semantic Web—and the Labelled Property Graph (LPG)—the choice of modern AI engineers. We will see why the LPG is the "Secret Sauce" for RAG and when you might still want the rigid, academic rigor of an RDF Knowledge Graph.
1. The Resource Description Framework (RDF)
Concept: Everything is a "Triplet" (Subject, Predicate, Object).
- Format:
(Sudeep) (LIVES_IN) (London) - Query Language: SPARQL.
- Strength: Standardized and interoperable. You can link your RDF graph to the "Global Wikipedia Graph" (DBpedia) with zero effort.
The Challenge for RAG: RDF is "Flat." To add a property (like since: 2015), you have to create a new node or use a complex technique called "Reification." This makes the graph massive and slow to traverse for AI agents.
2. The Labelled Property Graph (LPG)
Concept: Native Nodes and Edges with "Properties" attached directly to them.
- Format:
(Sudeep {age: 32}) -[:LIVES_IN {since: 2015}]-> (London) - Query Language: Cypher (Neo4j) or Gremlin.
- Strength: High performance and extremely intuitive. It looks like a whiteboard drawing.
The Advantage for RAG: LPGs are much faster for the "Neighborhood Expansion" and "Pathfinding" tasks we covered in Lesson 5. Because the attributes (Adjectives) are stored inside the node, the traversal engine doesn't have to jump to separate nodes to find them.
3. Side-by-Side Comparison
| Feature | RDF (Knowledge Graph) | LPG (Property Graph) |
|---|---|---|
| Atomic Unit | The Triplet | Node / Edge / Property |
| Interoperability | High (Global Standards) | Low (Vendor Specific) |
| Logic | Formal Inference (Ontologies) | Structural Traversal |
| Performance | Slower for complex paths | Faster for complex paths |
| AI Experience | Hard for LLMs to generate SPARQL | Easy for LLMs to generate Cypher |
4. Why Graph RAG Prefers LPG (Property Graphs)
If you are building an AI agent today, you will almost certainly use a Property Graph.
- Context Density: You can pack a lot of metadata into a single node. This keeps your "Retrieval Results" compact and readable for the LLM.
- Cypher Simplicity: Cypher is a "Pattern Matching" language. It is very similar to ASCII art:
MATCH (p:Person)-[:WORKS_AT]->(c:Company). LLMs are incredibly good at writing Cypher. They struggle with the academic syntax of SPARQL.
5. Summary and Exercises
The choice between RDF and LPG is a choice between Interoperability and Performance.
- RDF is for the "Semantic Web" and global data sharing.
- LPG is for "Enterprise Intelligence" and high-speed AI reasoning.
- Property Graphs are the industry standard for Graph RAG.
Exercises
- Modeling Choice: You are building a bot that needs to combine data from 10 different public government databases. Which model (RDF or LPG) would clarify the "Interoperability"?
- Cypher vs. SPARQL: Look up a simple SPARQL query and a simple Cypher query for "Friends of Friends." Which one is easier for a human to read? Which one do you think an AI would be more likely to get right on the first try?
- Property Extraction: If you have a node for a "Car," where would you store the "Model Year" in an LPG? Where would it go in an RDF?
Congratulations! You have completed Module 4: Graph Fundamentals for AI Engineers. You now have the mathematical and architectural language to start Designing the Knowledge Graph Layer in Module 5!