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arXiv · 1706.02909

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
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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.

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Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva, Amal Shehan Perera, Keet Sugathadasa, Buddhi Ayesha. 2017-06-08. Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings. https://doi.org/10.1109/intech.2017.8102426

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