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

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 Protege 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.