Our Research
Exploring the intersection of traditional herbal knowledge, modern science, and artificial intelligence to advance phytopharmacology.
Research Focus Areas
Our interdisciplinary team works across several key areas to develop comprehensive knowledge systems for phytopharmacology
Biomedical Ontologies
Our team develops structured ontologies that bridge traditional herbal knowledge with modern biomedical concepts. By leveraging standard biomedical ontologies like MeSH, UMLS, and ChEBI, we create a rich semantic network linking medicinal plants to their constituents, indications, and related concepts.
These ontologies enable the integration of herbal medicine data with conventional medical systems, supporting evidence-based phytotherapy and facilitating drug discovery from natural sources.
Key Projects
Herbal Medicine Ontology
Developing a comprehensive ontology aligned with disease and symptom terminologies
Phytochemical Classification
Creating structured hierarchies of plant compounds and their properties
Taxonomic Integration
Aligning plant nomenclature across different systems and languages
Applications of Our Research
Our work has practical applications across healthcare, pharmaceutical research, and education
Our knowledge systems provide healthcare professionals with reliable information on herbal treatments, enabling evidence-based recommendations and safer integration with conventional medicine.
By linking traditional herbal knowledge with modern molecular data, our research facilitates the identification of novel therapeutic compounds and drug candidates from plant sources.
Our AI-powered tools integrate with clinical systems to provide real-time guidance on herbal medicine use, including potential interactions, contraindications, and personalized recommendations.
Featured Research Projects
Structured Ontologies for Phytopharmacology
This flagship project focuses on developing comprehensive ontologies that bridge traditional herbal knowledge with modern biomedical concepts. By leveraging standard biomedical ontologies like MeSH, UMLS, and ChEBI, we create a rich semantic network linking medicinal plants to their constituents, indications, and related concepts.
HerbKG: AI-Constructed Knowledge Graph
HerbKG is an innovative knowledge graph built using deep learning to extract relationships from scientific literature. The system identifies connections between herbs, chemical compounds, diseases, and genes, creating a comprehensive network that supports research and clinical applications.
Collaborate With Us
We welcome collaborations with researchers, healthcare institutions, and industry partners interested in advancing phytopharmacology