Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through the application of machine learning. The use of data spaces, where data is exchanged among members in a self-regulated environment, is becoming increasingly popular. However, the current manual practices of managing metadata and vocabularies in these spaces are time-consuming, prone to errors, and may not meet the needs of all stakeholders. By leveraging the power of machine learning, we believe that semantic interoperability in data spaces can be significantly improved. This involves automatically generating and updating metadata, which results in a more flexible vocabulary that can accommodate the diverse terminologies used by different sub-communities. Our vision for the future of data spaces addresses the limitations of conventional data exchange and makes data more accessible and valuable for all members of the community.

翻译：我们的远景论文概述了一项通过应用机器学习提升数据空间语义互操作性未来的计划。数据空间作为一种在自我监管环境中成员间进行数据交换的模式，其应用日益普及。然而，当前在数据空间中依赖人工管理元数据与词汇的做法耗时费力、易出错，且可能无法满足所有利益相关者的需求。我们相信，借助机器学习的力量，数据空间中的语义互操作性可得到显著提升。具体而言，可自动生成并更新元数据，从而构建更为灵活的词汇表，以适应不同子群体使用的多样化术语体系。我们对数据空间未来的设想旨在克服传统数据交换的局限性，使社区所有成员更方便地获取数据并提升数据价值。