Ontology Learning from Twitter Data

Saad Alajlan, Frans Coenen, Boris Konev, Angrosh Mandya

2019

Abstract

This paper presents and compares three mechanisms for learning an ontology describing a domain of discoursed as defined in a collection of tweets. The task in part involves the identification of entities and relations in the free text data, which can then be used to produce a set of RDF triples from which an ontology can be generated. The first mechanism is therefore founded on the Stanford CoreNLP Toolkit.; in particular the Named Entity Recognition and Relation Extraction mechanisms that come with this tool kit. The second is founded on the GATE General Architecture for Text Engineering which provides an alternative mechanism for relation extraction from text. Both require a substantial amount of training data. To reduce the training data requirement the third mechanism is founded on the concept of Regular Expressions extracted from a training data “seed set”. Although the third mechanism still requires training data the amount of training data is significantly reduced without adversely affecting the quality of the ontologies generated.

Paper Citation

in Harvard Style

Alajlan S., Coenen F., Konev B. and Mandya A. (2019). Ontology Learning from Twitter Data. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 2: KEOD; ISBN 978-989-758-382-7, SciTePress, pages 94-103. DOI: 10.5220/0008067600940103

in Bibtex Style

@conference{keod19,
author={Saad Alajlan and Frans Coenen and Boris Konev and Angrosh Mandya},
title={Ontology Learning from Twitter Data},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 2: KEOD},
year={2019},
pages={94-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008067600940103},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 2: KEOD
TI - Ontology Learning from Twitter Data
SN - 978-989-758-382-7
AU - Alajlan S.
AU - Coenen F.
AU - Konev B.
AU - Mandya A.
PY - 2019
SP - 94
EP - 103
DO - 10.5220/0008067600940103
PB - SciTePress