Modelling weightlifting 'Training-Diet-Competition' cycle following a modular and scalable approach. (26th January 2021)

• Record Type: Journal Article
• Authors: Tumnark, Piyaporn; Cardoso, Paulo; Cabral, Jorge; Conceição, Filipe
• Is Part Of: International journal of metadata, semantics and ontologies. Volume 14:Number 3(2020)
• Page Start: 185
• Page End: 196
• Publication Date: 2021-01-26
• Subjects: ontology -- nutrition -- weightlifting -- biomechanics -- semantics -- reasoning; Metadata -- Periodicals; Semantic Web -- Periodicals; Ontologies (Information retrieval) -- Periodicals; Data structures (Computer science) -- Periodicals; Information theory -- Periodicals; 005.74
• Languages: English
• ISSNs: 1744-2621

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.