  Selecting a representative vector for a set of vectors is a very common
requirement in many algorithmic tasks. Traditionally, the mean or median vector
is selected. Ontology classes are sets of homogeneous instance objects that can
be converted to a vector space by word vector embeddings. This study proposes a
methodology to derive a representative vector for ontology classes whose
instances were converted to the vector space. We start by deriving five
candidate vectors which are then used to train a machine learning model that
would calculate a representative vector for the class. We show that our
methodology out-performs the traditional mean and median vector
representations.
