The Bloomberg Knowledge Graph is a graph-centric representation of entities and relationships in the financial world which connects cross-domain data from various sources within Bloomberg. Recent developments in machine learning, knowledge graphs, and language technology have enabled intelligent ways to uncover interesting patterns amongst data that reveal previously hidden insights. By leveraging the entity and relationship information in the knowledge graph, interesting potential applications emerge, especially when combined with other information such as market data and news stories. This talk details how Bloomberg uses the knowledge graph and semantic technologies to enable various use cases, e.g., to link data across different domains, enrich news stories, and support financial analytics centered around entities. In addition, we will discuss the challenges we face to support these use cases, including representing and storing historical, point-in-time relationships between entities.