DeepAI AI Chat
Log In Sign Up

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

by   Vindula Jayawardana, et al.

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.


page 1

page 2

page 3

page 4


Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons

We propose post-processing method for enriching not only word representa...

Neural Vector Conceptualization for Word Vector Space Interpretation

Distributed word vector spaces are considered hard to interpret which hi...

Evaluating vector-space models of analogy

Vector-space representations provide geometric tools for reasoning about...

Analyzing Structures in the Semantic Vector Space: A Framework for Decomposing Word Embeddings

Word embeddings are rich word representations, which in combination with...

A Visual Embedding for the Unsupervised Extraction of Abstract Semantics

Vector-space word representations obtained from neural network models ha...

Key Phrase Extraction Applause Prediction

With the increase in content availability over the internet it is very d...

Build2Vec: Building Representation in Vector Space

In this paper, we represent a methodology of a graph embeddings algorith...