Knowledge gathering, representation, and reasoning are among the fundamental challenges of artificial intelligence. Large-scale repositories of knowledge about entities, relations, and their abstractions are known as knowledge bases . Most major technology companies now have substantial efforts in knowledge base construction, and related scholarly work spans many research areas, including machine learning, deep learning, natural language processing, information integration, databases, search, data mining, knowledge representation, human computation, human-computer interfaces, and fairness. Recently there has been a surge of interest combined neural and symbolic representations that support queries and reasoning. In this course, we will study topics related to knowledge base construction and use, including entity recognition, entity resolution, relation extraction, embedded representations for knowledge, data structures for knowledge indexing, reasoning, semantic parsing, and question answering. There will be special focus on recent research in all of these areas. The course will consist of lectures, paper presentations, semester-long research projects. 3 credits.