The advent of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web sources has enabled the automatic construction of large knowledge bases. Recent endeavors of this kind include academic research projects such as DBpedia, EntityCube, KnowItAll, ReadTheWeb, and YAGO-NAGA, as well as industrial ones such as Freebase and Trueknowledge. These projects provide automatically-constructed, large and rich knowledge bases of facts about named entities, their semantic classes, and their mutual relations. This 1-day tutorial will discuss a) the content, organization, and potential of these Web-induced knowledge bases, b) state-of-the-art methods for constructing them from semistructured and textual Web sources, c) recent approaches to maintaining and extending them, which includes introducing a temporal dimension of knowledge, d) use cases of knowledge bases, including semantic search, reasoning for question answering, and entity linking and disambiguation. It is likely to interest a broad audience of AI researchers because it bridges the areas of data and text mining, knowledge extraction, knowledge-based search, and uncertain data management. It will also point out open problems and research opportunities on this spectrum of issues.