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.