Information Retrieval and Machine Learning Methods for Academic Expert Finding
Metadatos
Afficher la notice complèteAuteur
de Campos, Luis M.; Fernández Luna, Juan Manuel; Huete Guadix, Juan Francisco; Ribadas Pena, Francisco J.; Bolaños, NéstorEditorial
MDPI
Materia
Expert finding Information retrieval Machine learning
Date
2024-01-23Referencia bibliográfica
de Campos, L.M.; Fernández-Luna, J.M.; Huete, J.F.; Ribadas-Pena, F.J.; Bolaños, N. Information Retrieval and Machine Learning Methods for Academic Expert Finding. Algorithms 2024, 17, 51. https://doi.org/10.3390/a17020051
Patrocinador
Spanish “Agencia Estatal de Investigación” under grants PID2019-106758GB-C31 and PID2020-113230RB-C22; Spanish “FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades” under grant A-TIC-146-UGR20; European Regional Development Fund (ERDF-FEDER)Résumé
In the context of academic expert finding, this paper investigates and compares the
performance of information retrieval (IR) and machine learning (ML) methods, including deep
learning, to approach the problem of identifying academic figures who are experts in different
domains when a potential user requests their expertise. IR-based methods construct multifaceted
textual profiles for each expert by clustering information from their scientific publications. Several
methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods
treat expert finding as a classification task, training automatic text classifiers using publications
authored by experts. By comparing these approaches, we contribute to a deeper understanding of
academic-expert-finding techniques and their applicability in knowledge discovery. These methods
are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results
show how IR techniques were, in general, more robust with both datasets and more suitable than the
ML-based ones, with some exceptions showing good performance.