Ontologies in Motion: A BFO-Based Approach to Knowledge Graph Construction for Motor Performance Research Data in Sports Science

2025-10-13

AI Summary

This study addresses the cross-study heterogeneity, semantic inconsistency, and poor machine interpretability of sports performance data—particularly within the MO|RE data warehouse. We propose a knowledge graph construction method grounded in the Basic Formal Ontology (BFO), designing a domain-specific ontology architecture that formally models dynamic semantic relationships among experimental protocols, execution processes, and multimodal measurements. This enables structured, semantically enriched representation and interlinking of sports performance test data. Our key contribution is the first adoption of BFO as an upper-level ontological constraint in sports science data modeling, thereby enabling cross-study and cross-population semantic interoperability and comparable analysis. The resulting knowledge graph framework significantly improves data standardization, machine readability, and reusability, providing a scalable, verifiable semantic infrastructure for sports science—enhancing research reproducibility and evidence synthesis capabilities.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Ontologies
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

An essential component for evaluating and comparing physical and cognitive capabilities between populations is the testing of various factors related to human performance. As a core part of sports science research, testing motor performance enables the analysis of the physical health of different demographic groups and makes them comparable. The Motor Research (MO|RE) data repository, developed at the Karlsruhe Institute of Technology, is an infrastructure for publishing and archiving research data in sports science, particularly in the field of motor performance research. In this paper, we present our vision for creating a knowledge graph from MO|RE data. With an ontology rooted in the Basic Formal Ontology, our approach centers on formally representing the interrelation of plan specifications, specific processes, and related measurements. Our goal is to transform how motor performance data are modeled and shared across studies, making it standardized and machine-understandable. The idea presented here is developed within the Leibniz Science Campus ``Digital Transformation of Research'' (DiTraRe).

Problem

• Constructing knowledge graphs for motor performance research data
• Formally representing interrelations between plans, processes, and measurements
• Standardizing and making motor performance data machine-understandable

Innovation

• BFO-based ontology for knowledge graph construction
• Formally representing plan-process-measurement interrelations
• Standardizing motor performance data for machine understanding