Title: Modelling weightlifting 'Training-Diet-Competition' cycle following a modular and scalable approach
Authors: Piyaporn Tumnark; Paulo Cardoso; Jorge Cabral; Filipe Conceição
Addresses: Faculty of Sport, University of Porto, Porto, Portugal; Faculty of Sports Science, Kasetsart University, Kamphaeng Saen Campus, Nakorn Pathom, Thailand ' Industrial Electronics Department, University of Minho, Braga, Portugal ' Industrial Electronics Department, University of Minho, Braga, Portugal ' Faculty of Sport, University of Porto, Porto, Portugal
Abstract: Studies in weightlifting have been characterised by unclear results and information paucity, mainly due to the lack of information sharing between athletes, coaches, biomechanists, physiologists and nutritionists. These experts' knowledge is not captured, classified or integrated into an information system for decision-making. An ontology-driven knowledge model for Olympic weightlifting was developed to leverage a better understanding of the weightlifting domain as a whole, bringing together related knowledge domains of training methodology, weightlifting biomechanics, and dietary regimes, while modelling the synergy among them. It unifies terminology, semantics, and concepts among sport scientists, coaches, nutritionists, and athletes to partially obviate the recognised limitations and inconsistencies, leading to the provision of superior coaching and a research environment which promotes better understanding and more conclusive results. The ontology-assisted weightlifting knowledge base consists of 110 classes, 50 object properties, 92 data properties, 167 inheritance relationships concepts, in a total of 1761 axioms, alongside 23 SWRL rules.
Keywords: ontology; nutrition; weightlifting; biomechanics; semantics; reasoning.
International Journal of Metadata, Semantics and Ontologies, 2020 Vol.14 No.3, pp.185 - 196
Accepted: 30 Apr 2020
Published online: 26 Jan 2021 *