Semantic Enrichment of Moroccan Tourism Ontology Based NLP Technologies

Authors

ASMA AMALKI Image and Pattern Recognition – Intelligent and Communicating Systems Laboratory (IRF-SIC), Faculty of Science, Ibn Zohr University, Agadir, Morocco
KHALID TATANE National School of Applied Sciences, ESTIDMA research team, Ibn Zohr University, Agadir, Morocco
ALI BOUZIT Image and Pattern Recognition – Intelligent and Communicating Systems Laboratory (IRF-SIC), Faculty of Science, Ibn Zohr University, Agadir, Morocco

Abstract

Ontology is a conceptual representation model that allows sharing and reuse of a domain knowledge in a human and machine-readable format. However, the massive amount of knowledge available today makes ontology enrichment a challenging task. In this paper, we present a semi-automatic approach of ontology learning from collection of domain specific texts, to text processing based NLP tools, to enrich an existing ontology. This study utilize data crawling from official websites. Concepts and relations extraction is done automatically from a textual corpus and domain experts do consistency and redundancy check manually. The proposed approach is applied in order to enrich Moroccan tourism ontology named OTM with semantic entities extracted from heterogeneous data sources.

Keywords: Corpus, Knowledge acquisition, NLP, ontology enrichment, ontology learning.

Received Date: April 11, 2024 Accepted Date: May 10, 2024

Published Date: June 01, 2024

Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/155

DOI: https://doi.org/10.5281/zenodo.11244423

Downloads

Published

2024-06-01

How to Cite

ASMA AMALKI, KHALID TATANE, & ALI BOUZIT. (2024). Semantic Enrichment of Moroccan Tourism Ontology Based NLP Technologies. International Journal of Scientific Research and Innovative Studies, 3(3), 41–45. Retrieved from http://ijsrisjournal.com/index.php/ojsfiles/article/view/155