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.

翻译：数据空间是一种新兴概念，旨在实现基于数据的应用和商业模式的可信实施，为所有利益相关者提供高度灵活性和主权。目前，数据空间在移动出行、健康、食品等不同领域逐步兴起，因此需要识别并实现语义接口，以确保这些数据空间的技术互操作性。本文整合了来自13个不同领域的数据模型，分析了这些领域的本体论不协调性。通过使用网络图，识别出核心数据模型和本体属性，并定性描述了这些领域的语义异质性。研究展望部分阐述了这些结果如何帮助跨领域连接不同的数据空间。