A knowledge graph and a traditional database serve different purposes and have distinct structural characteristics. A traditional database, like a relational database, organizes data into predefined tables with rows and columns. Each table has a specific schema, which defines the types of data it can hold and the relationships among tables. For instance, if you have a database for a book store, you might have tables for authors, books, and customers, with foreign keys to link them based on their relationships.
In contrast, a knowledge graph is designed to represent information in a more flexible and interconnected way. It organizes data as a network of entities and their relationships. Each entity, or node, can have multiple attributes and can connect to other entities with edges that define the nature of the relationship. Using the book store example, instead of keeping authors and books in separate tables, a knowledge graph would allow you to represent an author as a node that connects directly to the books they have written. This flexibility makes it easier to express complex relationships without rigid schemas.
Additionally, querying a knowledge graph is often more intuitive when working with interconnected data. Traditional databases typically use SQL for querying, which can require complex joins to retrieve related information. In contrast, knowledge graphs often use graph query languages like SPARQL or Gremlin, allowing for simpler and more direct queries regarding relationships among data. For example, you could easily ask which authors have written books in a certain genre without having to deal with multiple joins in SQL. This makes knowledge graphs particularly useful for applications such as recommendation systems and semantic search, where understanding the connections between entities is essential.