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，这种多样性给流式处理解决方案的讨论与整合带来了重大挑战。本研究尝试通过一种新颖的分类法系统化文献中存在的RDF流类型，以填补这一关键研究空白。所提出的RDF流分类法（RDF-STaX）具体化为遵循FAIR原则的OWL 2 DL本体，使其易于实际应用。为促进该本体的采用，提供了详尽的文档和补充资源。通过展示两个已实现的使用案例，证明了该资源在讨论研究工作与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，它作为一份协作式、动态更新的RDF流式处理前沿综述。RDF-STaX旨在满足学术界对更高效系统化与描述RDF流的迫切需求。该资源通过促进科学讨论、合作与工具互操作性，旨在推动RDF流式处理领域的创新。