Knowledge graph vs database
Databases store records for retrieval. Knowledge graphs store meaning for reasoning and traversal
Direct answer
Knowledge graphs and databases serve different purposes. Databases store records for retrieval and transactions. Knowledge graphs store meaning for reasoning and traversal.
The structural difference determines when each architecture fits. Databases optimise for predefined queries against structured records. Knowledge graphs optimise for exploratory queries across semantic relationships.
Core difference
Databases organise information into tables, rows, and columns. Relationships between tables exist through foreign keys and joins defined by the database schema. The meaning of relationships is implicit in schema design and application logic.
Knowledge graphs organise information as entities and typed relationships. Relationships carry explicit semantic meaning independent of any application. The meaning is encoded in the relationship type itself, not inferred from schema design.
This matters because databases require predefined query paths through joins, while knowledge graphs allow machines to traverse relationships dynamically without predetermined paths.
Comparison table
| Feature | Knowledge Graph | Database |
|---|---|---|
| Structure | Entities + typed relationships | Tables + rows + columns |
| Relationships | Explicit semantic connections | Implicit foreign key joins |
| Query model | Traverse relationship networks | Execute predefined joins |
| Schema flexibility | Add entities and relationships without restructuring | Schema changes require table alterations |
| Maintenance overhead | Relationship changes update graph logic | Schema changes require developer intervention |
| Explainability | Relationship types explain connections | Join logic remains in application code |
| Best for | Complex domain modelling, AI reasoning | Transaction processing, reporting |
When databases are appropriate
Databases fit when:
- Information follows predictable tabular structures (customers, orders, invoices)
- Query patterns are known in advance and change infrequently
- Transactional integrity matters more than semantic flexibility
- The domain involves high-volume routine operations rather than complex relationship reasoning
Examples include financial transactions, inventory management, and operational reporting where the same queries run repeatedly against stable schemas.
When knowledge graphs are appropriate
Knowledge graphs fit when:
- Relationships between entities carry semantic meaning that changes frequently
- Query patterns cannot be predetermined because exploration matters more than retrieval
- Multiple relationship types connect the same entities in different ways
- AI systems need to reason over domain knowledge without human interpretation
Examples include supplier networks, material applications, research knowledge, and any domain where understanding how things relate matters more than processing transactions.
How they work together
Knowledge graphs and databases often operate as complementary layers rather than alternatives.
Databases handle transactional workloads: order processing, user authentication, audit trails. Knowledge graphs handle semantic workloads: entity disambiguation, relationship reasoning, AI-driven discovery.
A common pattern sees databases storing operational records while knowledge graphs store the semantic relationships between entities referenced in those records. The database processes transactions. The knowledge graph helps AI systems understand what those transactions mean.
This separation allows each system to optimise for its strength without compromising the other.