Optimization of Methods for Querying Formal Ontologies in Natural Language Using a Neural Network

Anicet Ondo, Laurence Capus, Mamadou Bousso

2024

Abstract

A well-designed ontology must be capable of addressing all the needs it is intended to satisfy. This complex task involves gathering all the potential questions from future users that the ontology should answer in order to respond precisely to these requests. However, variations in the questions asked by users for the same need complicate the interrogation process. Consequently, the use of a question-answering system seems to be a more efficient option for translating user queries into the formal SPARQL language. Current methods face significant challenges, including their reliance on predefined patterns, the quality of models and training data, ontology structure, resource complexity for approaches integrating various techniques, and their sensitivity to linguistic variations for the same user need. To overcome these limitations, we propose an optimal classification approach to classify user queries into corresponding SPARQL query classes. This method uses a neural network based on Transformer encoder-decoder architectures, improving both the understanding and generation of SPARQL queries while better adapting to variations in user queries. We have developed a dataset on estate liquidation and Python programming, built from raw data collected from specialist forums and websites. Two transformer models, GPT-2 and T5, were evaluated, with the basic T5 model obtaining a satisfactory score of 97.22%.

Paper Citation

in Harvard Style

Ondo A., Capus L. and Bousso M. (2024). Optimization of Methods for Querying Formal Ontologies in Natural Language Using a Neural Network. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-716-0, SciTePress, pages 119-126. DOI: 10.5220/0012892600003838

in Bibtex Style

@conference{keod24,
author={Anicet Ondo and Laurence Capus and Mamadou Bousso},
title={Optimization of Methods for Querying Formal Ontologies in Natural Language Using a Neural Network},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2024},
pages={119-126},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012892600003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Optimization of Methods for Querying Formal Ontologies in Natural Language Using a Neural Network
SN - 978-989-758-716-0
AU - Ondo A.
AU - Capus L.
AU - Bousso M.
PY - 2024
SP - 119
EP - 126
DO - 10.5220/0012892600003838
PB - SciTePress