What is the function of a knowledge graph in AI?

A knowledge graph plays a crucial role in artificial intelligence by connecting various concepts and entities, which facilitates the extraction of insights and relationships from the data it encompasses. This interconnected structure allows AI systems to understand and reason about the information effectively, leading to improved decision-making and problem-solving capabilities.

In a knowledge graph, entities such as people, places, and objects are represented as nodes, while the relationships between them are depicted as edges. This enables the graph to illustrate how different items are related, making it easy to query and analyze the information in a more meaningful way.

The focus of the knowledge graph on relationships and contextual understanding differentiates it from storage solutions designed primarily for unstructured data, mere data visualization, or the creation of programming interfaces. While these other functions are valuable, they do not encapsulate the primary purpose of a knowledge graph, which is all about enhancing the way AI can perceive and infer knowledge from complex interdependencies. This makes the connectivity and insights derived from a knowledge graph essential for advancing AI applications.