Onto.KOM - Towards a Minimally Supervised Ontology Learning System based on Word Embeddings and Convolutional Neural Networks

Wael Alkhatib, Leon Alexander Herrmann, Christoph Rensing

2017

Abstract

This paper introduces Onto.KOM: a minimally supervised ontology learning system which minimizes the reliance on complicated feature engineering and supervised linguistic modules for constructing the different consecutive components of an ontology, potentially providing domain independent and fully automatic ontology learning system. The focus here is to fill in the gap between automatically identifying the different ontological categories reflecting the domain of interest and the extraction and classification of semantic relations between the concepts under the different categories. In Onto.KOM, we depart from traditional approaches with intensive linguistic analysis and manual feature engineering for relation classification by introducing a convolutional neural network (CNN) that automatically learns features from word-pair offset in the vector space. The experimental results show that our system outperforms the state-of-the-art systems for relation classification in terms of F1-measure.

Paper Citation

in Harvard Style

Alkhatib W., Alexander Herrmann L. and Rensing C. (2017). Onto.KOM - Towards a Minimally Supervised Ontology Learning System based on Word Embeddings and Convolutional Neural Networks.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-272-1, pages 17-26. DOI: 10.5220/0006483000170026

in Bibtex Style

@conference{keod17,
author={Wael Alkhatib and Leon Alexander Herrmann and Christoph Rensing},
title={Onto.KOM - Towards a Minimally Supervised Ontology Learning System based on Word Embeddings and Convolutional Neural Networks},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,},
year={2017},
pages={17-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006483000170026},
isbn={978-989-758-272-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,
TI - Onto.KOM - Towards a Minimally Supervised Ontology Learning System based on Word Embeddings and Convolutional Neural Networks
SN - 978-989-758-272-1
AU - Alkhatib W.
AU - Alexander Herrmann L.
AU - Rensing C.
PY - 2017
SP - 17
EP - 26
DO - 10.5220/0006483000170026