The process of searching and understanding existing vocabularies (terminological artifacts) on the Linked Data Web is an intrinsic activity to the consumption and production of Linked Data. Data consumers trying to ﬁnd and understand the vocabularies behind datasets in order to query them, or data producers searching for existing resources to describe their data, face the challenge of semantically searching existing concepts in vocabularies. Traditional search mechanisms do not address the level of semantic matching necessary to match users’ information needs to vocabulary elements, bringing an additional barrier to the consumption and production of Linked Data on the Web. This work describes a terminological search mechanism which uses a distributional semantic model to provide a best-eﬀort semantic matching solution. The distributional semantic model leverages the semantic information present in large volumes of unstructured text to improve the semantic matching capabilities of the search process. A quantitative evaluation of the quality of the search results shows that the approach provides an eﬀective semantic matching mechanism for terminological search.