Towards solving ontological dissonance using network graphs

TL;DR

This paper investigates ontological dissonance across diverse Data Spaces by analyzing data models from 13 domains using network graphs, aiming to improve semantic interoperability and cross-domain connectivity.

Contribution

It consolidates data models from multiple domains and applies network graph analysis to identify key ontological attributes and address semantic heterogeneity.

Findings

Identification of central data models and ontology attributes

Qualitative description of semantic heterogeneity

Insights for connecting Data Spaces across domains

Abstract

Data Spaces are an emerging concept for the trusted implementation of data-based applications and business models, offering a high degree of flexibility and sovereignty to all stakeholders. As Data Spaces are currently emerging in different domains such as mobility, health or food, semantic interfaces need to be identified and implemented to ensure the technical interoperability of these Data Spaces. This paper consolidates data models from 13 different domains and analyzes the ontological dissonance of these domains. Using a network graph, central data models and ontology attributes are identified, while the semantic heterogeneity of these domains is described qualitatively. The research outlook describes how these results help to connect different Data Spaces across domains.