What are Knowledge Graphs?
Knowledge Graphs are a structured representation of knowledge that consists of entities, relationships, and attributes. They are used to store, organize, and retrieve information in a way that is both human-readable and machine-processable. Knowledge Graphs are used in various applications, such as semantic search, recommendation systems, and natural language processing tasks.
How do Knowledge Graphs work?
Knowledge Graphs consist of nodes and edges, where nodes represent entities (such as people, places, or concepts), and edges represent relationships between those entities. They are often built using RDF (Resource Description Framework) or similar graph-based data models, which enable the representation of complex relationships and allow for advanced querying and reasoning capabilities.
Example of a Knowledge Graph
Consider a Knowledge Graph that represents information about famous scientists and their discoveries. Nodes in this graph could represent people (e.g., “Albert Einstein”), discoveries (e.g., “General Theory of Relativity”), and organizations (e.g., “Princeton University”). Edges between these nodes could represent relationships such as “discovered by,” “affiliated with,” or “collaborated with.”
