Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings
This addresses a common algorithmic need for ontology processing, but it is incremental as it builds on existing vector embedding techniques.
The study tackled the problem of selecting a representative vector for ontology classes by proposing a machine learning-based methodology that uses five candidate vectors, and it outperformed traditional mean and median representations.
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.