Twine serviced information storage, authoring and discovery through its website and browser-based tools. The service, intended for regular web users, attempted to automate certain processes related to data categorization and keyword-association (tagging). The system employed natural language processing and machine learning to extract concepts from written text in user data and express it using RDF triples tied to a semantic taxonomy based on concepts mined from Wikipedia. This made it easier for machines to process the data and enabled specifying types of information to search for on the Twine website, such as "person" or "location". Twine could be classified as a social network as it also had features such as adding contacts, sending private messages and sharing information.
