What is Knowledge Graph?
Knowledge Graph is a structured representation of real-world entities and their relationships, organized as a network of nodes (entities) and edges (relationships), enabling machines to understand and reason about interconnected information.
Quick Facts
| Created | Concept from 2000s, popularized by Google in 2012 |
|---|---|
| Specification | Official Specification |
How It Works
Knowledge graphs have become fundamental infrastructure for organizing and querying complex information. Popularized by Google in 2012, they represent facts as subject-predicate-object triples (e.g., 'Paris' - 'is capital of' - 'France'). Modern applications combine knowledge graphs with LLMs to provide factual grounding, reduce hallucinations, and enable complex reasoning. Major knowledge graphs include Wikidata, DBpedia, and enterprise-specific graphs used by companies like Google, Amazon, and LinkedIn.
Key Characteristics
- Represents entities and relationships as graph structures
- Uses subject-predicate-object triples for facts
- Enables semantic querying with languages like SPARQL
- Supports reasoning and inference over relationships
- Integrates with LLMs for grounded AI applications
- Scales to billions of facts and entities
Common Use Cases
- Search engine knowledge panels
- Recommendation systems with relationship awareness
- Question answering with factual grounding
- Drug discovery and biomedical research
- Enterprise knowledge management
Example
Loading code...Frequently Asked Questions
What is the difference between a knowledge graph and a database?
Knowledge graphs store data as interconnected entities and relationships (triples), enabling semantic queries and inference. Traditional databases store data in tables with fixed schemas. Knowledge graphs excel at representing complex relationships and enabling reasoning, while databases are optimized for structured data storage and transactional operations.
How do knowledge graphs reduce AI hallucinations?
Knowledge graphs provide factual grounding for LLMs by supplying verified, structured information. When integrated with RAG (Retrieval-Augmented Generation), they retrieve relevant facts that the LLM uses as context, reducing the chance of generating incorrect information. The graph's explicit relationships also help maintain logical consistency.
What is a triple in knowledge graph terminology?
A triple is the fundamental unit of knowledge representation, consisting of subject-predicate-object (e.g., 'Paris' - 'isCapitalOf' - 'France'). The subject is an entity, the predicate defines the relationship, and the object can be another entity or a literal value. Collections of triples form the graph structure.
What is SPARQL and how is it used with knowledge graphs?
SPARQL is a query language for RDF knowledge graphs, similar to SQL for relational databases. It allows you to query triple patterns, filter results, and traverse relationships. SPARQL enables complex queries like 'find all cities that are capitals of countries in Europe with population over 1 million'.
What are some popular knowledge graphs I can use?
Public knowledge graphs include Wikidata (structured Wikipedia data), DBpedia (extracted from Wikipedia), and Google's Knowledge Graph (powers search panels). For building custom graphs, popular tools include Neo4j (property graph database), Apache Jena (RDF framework), and Amazon Neptune (managed graph database).