Preprint Communication Version 2 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 20 April 2021 / Approved: 21 April 2021 / Online: 21 April 2021 (11:49:04 CEST)
Version 2 : Received: 21 June 2021 / Approved: 23 June 2021 / Online: 23 June 2021 (11:47:32 CEST)

A peer-reviewed article of this Preprint also exists.

Axiotis, E.; Kontogiannis, A.; Kalpoutzakis, E.; Giannakopoulos, G. A Personalized Machine-Learning-Enabled Method for Efficient Research in Ethnopharmacology. The Case of the Southern Balkans and the Coastal Zone of Asia Minor. Appl. Sci. 2021, 11, 5826. Axiotis, E.; Kontogiannis, A.; Kalpoutzakis, E.; Giannakopoulos, G. A Personalized Machine-Learning-Enabled Method for Efficient Research in Ethnopharmacology. The Case of the Southern Balkans and the Coastal Zone of Asia Minor. Appl. Sci. 2021, 11, 5826.

Journal reference: Appl. Sci. 2021, 11, 5826
DOI: 10.3390/app11135826

Abstract

Ethnopharmacology experts face 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 experts can be supported effectively through intelligent tools in the task of ethnopharmacological literature research. To this end, we utilize a real case study of ethnopharmacology research, aimed at the Southern Balkans and Coastal zone of Asia Minor. Thus, we propose a methodology for more efficient research in ethnopharmacology. Our work follows an “Expert-Apprentice” paradigm in an automatic URL extraction process, through crawling, 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.

Keywords

Ethnopharmacology; Artificial Intelligence; Web Crawling; Active Learning; Reinforcement Learning; Text Mining; Big Data

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (1)

Comment 1
Received: 23 June 2021
Commenter: Evangelos Axiotis
Commenter's Conflict of Interests: Author
Comment: The manuscript has been revised and accepted for publication to Applied Sciences.
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