Lecture
This lecture covers the concepts of information extraction and knowledge inference. Information extraction involves creating a matrix with entity pairs as rows and relation types as columns, extracting relations from text patterns and knowledge bases. Knowledge inference focuses on surface patterns, Bayesian personalized ranking, and relation embeddings. It also delves into taxonomy induction, entity disambiguation, and the discovery of concepts and terms. The instructor discusses the challenges of entity linking, homonyms, and synonyms, as well as the use of entity graphs and local information for interpreting mentions in a knowledge base.