ConceptNet

ConceptNet is a semantic network based on the information in the OMCS database. ConceptNet is expressed as a directed graph whose nodes are concepts, and whose edges are assertions of common sense about these concepts. Concepts represent sets of closely related natural language phrases, which could be noun phrases, verb phrases, adjective phrases, or clauses.[5] ConceptNet is created from the natural-language assertions in OMCS by matching them against patterns using a shallow parser... The information in ConceptNet can be used as a basis for machine learning algorithms. One representation, called AnalogySpace, uses singular value decomposition to generalize and represent patterns in the knowledge in ConceptNet, in a way that can be used in AI applications. Its creators distribute a Python machine learning toolkit called Divisi for performing machine learning based on text corpora, structured knowledge bases such as ConceptNet, and combinations of the two. https://en.wikipedia.org/wiki/Open_Mind_Common_Sense#ConceptNet

ConceptNet originated from the crowdsourcing project Open Mind Common Sense, which was launched in 1999 at the MIT Media Lab. It has since grown to include knowledge from other crowdsourced resources, expert-created resources, and games with a purpose... ConceptNet is used to create word embeddings -- representations of word meanings as vectors, similar to word2vec, GloVe, or fastText, but better. https://conceptnet.io/