Ontology-Powered Boosting for Improved Recognition of Ontology Concepts from Biological Literature

Pratik Devkota, Somya Mohanty, Prashanti Manda

2023

Abstract

Automated ontology curation involves developing machine learning models that can learn patterns from scientific literature to predict ontology concepts for pieces of text. Deep learning has been used in this area with promising results. However, these models often ignore the semantically rich information that’s embedded in the ontologies and treat ontology concepts as independent entities. Here, we present a novel approach called Ontology Boosting for improving prediction accuracy of automated curation techniques powered by deep learning. We evaluate the performance of our models using Jaccard semantic similarity – a metric designed to assess similarity between ontology concepts. Semantic similarity metrics have the capability to estimate partial similarity between ontology concepts thereby making them ideal for evaluating the performance of annotation systems such as deep learning where the goal is to get as close as possible to human performance. We use the CRAFT gold standard corpus for training our architectures and show that the Ontology Boosting approach results in substantial improvements in the performance of these architectures.

Paper Citation

in Harvard Style

Devkota P., Mohanty S. and Manda P. (2023). Ontology-Powered Boosting for Improved Recognition of Ontology Concepts from Biological Literature. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-631-6, SciTePress, pages 80-90. DOI: 10.5220/0011683200003414

in Bibtex Style

@conference{bioinformatics23,
author={Pratik Devkota and Somya Mohanty and Prashanti Manda},
title={Ontology-Powered Boosting for Improved Recognition of Ontology Concepts from Biological Literature},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS},
year={2023},
pages={80-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011683200003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS
TI - Ontology-Powered Boosting for Improved Recognition of Ontology Concepts from Biological Literature
SN - 978-989-758-631-6
AU - Devkota P.
AU - Mohanty S.
AU - Manda P.
PY - 2023
SP - 80
EP - 90
DO - 10.5220/0011683200003414
PB - SciTePress