Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal AI. Over last decade, large-scale bases, also known as graphs, have automatically constructed from web contents text sources, become key asset for search engines. This machine can be harnessed to semantically interpret textual phrases in news, social media tables, contributes question answering, natural language processing data analytics. article surveys fundamental concepts practical methods creating curating large bases. It covers models discovering canonicalizing semantic types organizing them into clean taxonomies. On top this, discusses automatic extraction entity-centric properties. To support long-term life-cycle quality assurance knowledge, presents constructing open schemas curation. Case studies on academic projects industrial graphs complement survey methods.