Over the years, RDF streaming was explored in research and practice from many angles, resulting in a wide range of RDF stream definitions. This variety presents a major challenge in discussing and integrating streaming solutions, due to the lack of a common language. This work attempts to address this critical research gap, by systematizing RDF stream types present in the literature in a novel taxonomy. The proposed RDF Stream Taxonomy (RDF-STaX) is embodied in an OWL 2 DL ontology that follows the FAIR principles, making it readily applicable in practice. Extensive documentation and additional resources are provided, to foster the adoption of the ontology. Two realized use cases are presented, demonstrating the usefulness of the resource in discussing research works and annotating streaming datasets. Another result of this contribution is the novel nanopublications dataset, which serves as a collaborative, living state-of-the-art review of RDF streaming. The aim of RDF-STaX is to address a real need of the community for a better way to systematize and describe RDF streams. The resource is designed to help drive innovation in RDF streaming, by fostering scientific discussion, cooperation, and tool interoperability.

翻译：多年来，RDF流式处理在研究与实践领域从多个角度得到探索，产生了多种多样的RDF流定义。由于缺乏通用语言，这种多样性给讨论和整合流式解决方案带来了重大挑战。本文尝试通过提出一种新颖的分类法，将文献中存在的RDF流类型进行系统化，以填补这一关键研究空白。所提出的RDF流分类法（RDF-STaX）以遵循FAIR原则的OWL 2 DL本体形式呈现，使其能够直接应用于实际场景。为促进该本体的采用，我们提供了详尽的文档和额外资源。文末展示的两个实际用例，证明了该资源在讨论研究工作和标注流式数据集方面的实用性。本研究的另一成果是新颖的纳米出版物数据集，它作为一项协作式的、持续更新的RDF流式处理研究综述。RDF-STaX的目标是满足社区对更好系统化与描述RDF流的真实需求。该资源旨在通过促进科学讨论、协作和工具互操作性，推动RDF流式处理的创新发展。