What is a knowledge graph used for in AI?

A knowledge graph serves as a structured representation of entities and their interrelations, making option B the correct choice. In the context of artificial intelligence, it facilitates the organization and integration of information in a way that machines can understand. By representing knowledge in the form of nodes (which represent entities) and edges (which represent the relationships between those entities), knowledge graphs enable AI systems to retrieve, infer, and reason about the data more effectively.

This structured format allows various AI applications — such as natural language processing, recommendation systems, and semantic search — to leverage the underlying connections among different pieces of information, leading to more accurate and contextualized outputs. The utilization of knowledge graphs is pivotal in enhancing the comprehension of complex data relationships across multiple domains, thus driving smarter decision-making and insights.

While the other options highlight important aspects of data processing and analytics, they do not encapsulate the primary function of a knowledge graph in AI. For instance, a model for predictive analytics pertains to statistical methods used for forecasting outcomes, a database for storing raw data focuses on data storage rather than relationships, and a visualization tool is centered around data depiction rather than structured interrelations.