A knowledge graph is a structured form of knowledge representation that organizes information in a network of nodes (entities) and edges (relationships). These entities can represent real objects such as people, places, companies or abstract concepts such as ideas and events. The relationships between the entities define how they are connected to each other and how they are related.
Main features of a knowledge graph
- Entities: These are the nodes in the graph. They represent the objects or concepts about which knowledge is stored.
- Relationships: These are the edges in the graph and describe how the entities are linked to each other. For example, a relationship "is a colleague of" could link two people.
- Attributes: Entities and relationships can have attributes that provide additional information. For example, a person (entity) could have an attribute such as "date of birth" or "place of residence".
- Ontology: This is the set of rules or structure that determines how entities and relationships are defined and organized. It determines the way in which the information in the graph can be categorized and used.
Applications of Knowledge Graphs
- Search engines: Search engines like Google use Knowledge Graphs to make search results more relevant and provide contextual answers by understanding the relationship between the terms searched.
- Data integration: Companies use knowledge graphs to integrate data from different sources and support complex queries that go beyond simple database queries.
- Personalization: In e-commerce or social media platforms, knowledge graphs are used to create personalized recommendations by understanding a user's connections and preferences.
A well-known example of a knowledge graph is the "Google Knowledge Graph", which prepares information on search queries and presents it in an info box. This graph links search terms with known entities and displays relevant, interlinked information.