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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

Ontological Relation Classification Using WordNet, Word Embeddings and Deep Neural Networks

verfasst von : Ahlem Chérifa Khadir, Ahmed Guessoum, Hassina Aliane

Erschienen in: Modelling and Implementation of Complex Systems

Verlag: Springer International Publishing

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Abstract

Learning ontological relations is an important step on the way to automatically developing ontologies. This paper introduces a novel way to exploit WordNet [16], the combination of pre-trained word embeddings and deep neural networks for the task of ontological relation classification. The data from WordNet and the knowledge encapsulated in the pre-trained word vectors are combined into an enriched dataset. In this dataset a pair of terms that are linked in WordNet through some ontological relation are represented by their word embeddings. A Deep Neural Network uses this dataset to learn the classification of ontological relations based on the word embeddings. The implementation of this approach has yielded encouraging results, which should help the ontology learning research community develop tools for ontological relation extraction.

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Vorheriges Kapitel Meta-learning to Select the Best Metaheuristic for the MaxSAT Problem
Nächstes Kapitel Gender Identification from Arabic Speech Using Machine Learning
Fußnoten
1
The “words” are actually terms, which could be multi-word terms. For the sake of simplicity, we use “word” to refer to term/lemma in this article.
 
7
For the hypernym class, we did not take all the available data in this experiment; this was done in order to make the data set more balanced with respect to the rest of the considered classes.
 
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Metadaten
Titel
Ontological Relation Classification Using WordNet, Word Embeddings and Deep Neural Networks
verfasst von
Ahlem Chérifa Khadir
Ahmed Guessoum
Hassina Aliane
Copyright-Jahr
2021
Buch
Modelling and Implementation of Complex Systems

Print ISBN: 978-3-030-58860-1

Electronic ISBN: 978-3-030-58861-8

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-58861-8_10

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