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

## TL;DR

This paper introduces a machine learning-based method to derive a more accurate representative vector for ontology classes from instance embeddings, outperforming traditional mean and median approaches.

## Contribution

It proposes a novel methodology that uses candidate vectors and machine learning to improve class vector representation in ontology embeddings.

## Key findings

- The machine learning approach outperforms mean and median vectors.
- The methodology provides more accurate class representations.
- Experimental results validate the effectiveness of the proposed method.

## Abstract

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.