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Authors: Wael Alkhatib ; Leon Alexander Herrmann and Christoph Rensing

Affiliation: TU Darmstadt, Germany

ISBN: 978-989-758-272-1

Keyword(s): Ontology, Neural Language Model, Word Embeddings, Ontology Enrichment, Convolutional Neural Network, Deep Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Engineering ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Knowledge Engineering and Ontology Development ; Knowledge Representation ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Engineering ; Symbolic Systems

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-meas ure. (More)

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Paper citation in several formats:
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

@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},
}

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

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