Entities in a knowledge graph are represented as nodes, which are the fundamental building blocks of the graph structure. Each node corresponds to a real-world object, concept, or event, such as people, places, organizations, or products. For instance, in a knowledge graph about movies, entities can include actors, movies, directors, and production studios. Each node is typically annotated with various attributes, or properties, that provide additional details about the entity. For example, a "Movie" node might include properties like "title," "release year," "genre," and "rating."
In addition to nodes, knowledge graphs utilize edges to represent the relationships between entities. These edges define how two or more nodes are interconnected, establishing the context in which they are related. For example, if there’s a relationship between an actor and a movie, an edge might indicate that the actor "starred in" that movie. This relational aspect of knowledge graphs allows for complex queries and reasoning, making it easier to infer new information from existing entities and their connections. A simple query might involve finding all actors who starred in a specific genre of movies, further illustrating the usefulness of this structure.
Furthermore, knowledge graphs may incorporate ontologies to standardize how entities and relationships are defined. This helps in maintaining consistency across the graph and provides a shared vocabulary for different applications. For instance, if a knowledge graph uses a defined ontology for movies, developers can rely on standard terms for properties like "release year" or relationships like "directed by." This consistency makes it easier to integrate data from multiple sources or to extend the graph with new entities without ambiguity. In summary, knowledge graphs use nodes and edges to represent entities and their relationships, allowing for meaningful data connections and organized knowledge representation.