This name is derived from LOD, standing for Linked Open Data. Let’s assume that the reader is somehow familiar with the latter concept, otherwise a visit to http://linkeddata.org/ or http://www.w3.org/2013/data/ will help to figure it before further reading.
Data on the Web use properties (aka predicates) and classes (aka types) to describe people, places, products, events, and any kind of things whatsoever. In the data « Mary is a person, her family name is Watson, she lives is the city of San Francisco », « Person » is the class of Mary, « City » is the class of San Francisco, « family name » and « lives is » are properties used to describe a person, the latter acting also as a link between a person and a place.
A vocabulary in LOV gathers definitions of a set of classes and properties (together simply called terms of the vocabulary), useful to describe specific types of things, or things in a given domain or industry, or things at large but for a specific usage. Terms of vocabularies also provide the links in linked data, in the above case between a Person and a City. The definitions of terms provided by the vocabularies bring clear semantics to descriptions and links, thanks to the formal language they use (some dialect of RDF such as RDFS or OWL). In short, vocabularies provide the semantic glue enabling Data to become meaningful Data.
The vocabulary collection is maintained by the LOV team of curators in charge of validating and inserting vocabularies in the LOV data base and assigning them a detailed review (updated on a yearly basis). Before a vocabulary is inserted, LOV team contacts the authors to make sure the vocabulary is published following the best practices and meets quality requirements of the overall LOV ecosystem. When some metadata failed to be extracted automatically (such as creators of a vocabulary), curators try to add them manually by harvesting information from the documentation or from direct communication with the publisher. Once included, an automatic script checks for vocabulary updates on a daily basis. The documentation assists any user in the task of understanding the semantics of each vocabulary term and therefore of any data using it. For instance, information about the creator and publisher is a key indication in case help or clarification is required, or to assess the stability of that artifact. About 55% of vocabularies specify at least one creator, contributor or editor. We augment this information using manually gathered information, leading to inclusion of data about the creator in over 85% of vocabularies in LOV. The database stores each version of a vocabulary over time since its first available issue. For each version, LOV stores a file backup on its server, even if the original files are no longer available from their original source. To embrace the complexity of the vocabulary ecosystem and assess the impact of a modification, one needs to know in which vocabularies and datasets a particular vocabulary term is referenced. LOV provides a unique entry point to such information.