Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings

Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva, Amal Shehan Perera, Keet Sugathadasa, Buddhi Ayesha
2017 Seventh International Conference on Innovative Computing Technology (INTECH)

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.

Keywords: Natural Language Processing | Machine Learning / Deep Learning | Ontologies | Law | Word Embedding | Word2vec | Representative Vector | Neural Networks |