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The creation of real-world Artificial Intelligence (AI) applications is dependent on leveraging a large volume of commonsense knowledge. Simple semantic interpretation tasks such as understanding that if 'A is married to B' then 'A is the spouse of B' or that 'car, vehicle, auto' have very similar meanings are examples of semantic approximation operations/inferences that are present in practically all applications of AI that interpret natural language.
Many AI applications depend on being semantically flexible, i.e. coping with the large vocabulary variation that is permitted by natural language. Sentiment Analysis, Question Answering, Information Extraction, Semantic Search and Classification are examples of tasks in which the ability to do semantic approximation is a central requirement.
Distributional Semantics Models and Word Vector Models emerged as successful approaches for supporting semantic approximations due to their ability to build comprehensive semantic approximation models and also to their simplicity of representation.
Indra is an efficient library and service to deliver word embeddings and semantic relatedness to industry-level applications offering 60+ pre-build models in 15 languages and several model algorithms and corpora. Indra is powered by spotify-annoy delivering an efficient approximate nearest neighbors function.