Information Retrieval and Machine Learning Methods for Academic Expert Finding

Author:

de Campos Luis M.1ORCID,Fernández-Luna Juan M.1ORCID,Huete Juan F.1ORCID,Ribadas-Pena Francisco J.2ORCID,Bolaños Néstor1

Affiliation:

1. Departamento de Ciencias de la Computación e Inteligencia Artificial, ETSI Informática y de Telecomunicación, CITIC-UGR, Universidad de Granada, 18071 Granada, Spain

2. Departamento de Informática, E.S. Enxeñaría Informática, Edificio Politécnico, Universidade de Vigo, 32004 Ourense, Spain

Abstract

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.

Funder

Agencia Estatal de Investigación

FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades

European Regional Development Fund

Publisher

MDPI AG

Reference80 articles.

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