A knowledge base (KB) is a computer-processable collection of knowledge about the world. In its simplest variant, a KB takes the form of a labeled graph, where the nodes are entities (such as people, organizations, and geographical locations), and the edges represent the links between these entities in the real world (such as who was born where, which organization is headed by whom, which city is the capital of which country etc.). Knowledge bases provide the background knowledge for different artificial intelligence applications, ranging from personal assistants to Web search, question answering, and text analysis. In particular, KBs are useful in information retrieval (IR), where they serve for structured search and entity disambiguation. Research has made extraordinary progress in the automated construction of KBs, and today's KBs contain billions of entities [1]. Nevertheless, KBs are still far from perfect. In this keynote talk, I outline several challenges in the construction and maintenance of KBs, and show how our research group approached them.