A Comprehensive and Scientifically Accurate Pharmaceutical Knowledge Ontology based on Multi-source Data

Pengfei Wang, Yiqing Mao, Wei Song, Wenting Jiang, Yang Liu, Liumeng Zheng, Bin Ma, Qingqing Sun, Sheng Liu

2022

Abstract

Recently, knowledge graphs have been applied by large pharmaceutical companies to improve the efficiency of drug discovery. Specifically, knowledge graphs based on drug ontology have been used for many purposes. Current drug ontologies have different scopes, but mainly focus on the description of basic drug information. Here, we describe a comprehensive pharmaceutical knowledge ontology, including information of active ingredients, indications, inactive ingredients, drugs, clinical trials, organs and tissues, literature, patents, targets, therapeutics, and biomolecules. Using multiple data sources, we apply a seven-step method for ontology modelling using Protégé software. A comprehensive pharmaceutical knowledge ontology model is established to complete the knowledge representation of drug information. By means of ontology theory, the pharmaceutical knowledge is modelled, standardized and networked, so as to clarify the knowledge structure and quickly acquire related knowledge and logical relationships. In the future, knowledge graphs based on this ontology could be helpful to deal with the dispersion, heterogeneity, redundancy and fragmentation of medical big data, to share and integrate pharmaceutical data, and to provide a set of solutions for the networked development of pharmaceutical knowledge.

Paper Citation

in Harvard Style

Wang P., Mao Y., Song W., Jiang W., Liu Y., Zheng L., Ma B., Sun Q. and Liu S. (2022). A Comprehensive and Scientifically Accurate Pharmaceutical Knowledge Ontology based on Multi-source Data. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-552-4, SciTePress, pages 168-175. DOI: 10.5220/0011012100003123

in Bibtex Style

@conference{bioinformatics22,
author={Pengfei Wang and Yiqing Mao and Wei Song and Wenting Jiang and Yang Liu and Liumeng Zheng and Bin Ma and Qingqing Sun and Sheng Liu},
title={A Comprehensive and Scientifically Accurate Pharmaceutical Knowledge Ontology based on Multi-source Data},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS},
year={2022},
pages={168-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011012100003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS
TI - A Comprehensive and Scientifically Accurate Pharmaceutical Knowledge Ontology based on Multi-source Data
SN - 978-989-758-552-4
AU - Wang P.
AU - Mao Y.
AU - Song W.
AU - Jiang W.
AU - Liu Y.
AU - Zheng L.
AU - Ma B.
AU - Sun Q.
AU - Liu S.
PY - 2022
SP - 168
EP - 175
DO - 10.5220/0011012100003123
PB - SciTePress