A Personalized Machine-Learning-enabled Method for Efficient Research in Ethnopharmacology. The case of Southern Balkans and Coastal zone of Asia Minor

An ethnopharmacology expert faces several challenges when identifying and retrieving documents and resources related to their scientific focus. The volume of sources that need to be monitored, the variety of formats utilized, the different quality of language use across sources, present some of what we call “big data” challenges in the analysis of this data. This study aims to understand if and how the expert can be supported effectively through intelligent tools for the ethnopharmacological research in the Southern Balkans and Coastal zone of Asia Minor. Our work follows an “Expert-Apprentice” paradigm in a crawling process, where the apprentice is a Machine Learning (ML) algorithm, utilizing a combination of Active Learning (AL) and Reinforcement Learning (RL), and the Expert is the human researcher. ML-powered research improved 3.1 times the effectiveness and 5.14 times the efficiency of the domain expert, fetching a total number of 420 relevant ethnopharmacological documents in only 7 hours versus an estimated 36-hour human-expert effort. Therefore, utilizing Artificial Intelligence (AI) tools to support the researcher can boost the efficiency and effectiveness of the identification and retrieval of appropriate documents.