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 systems, 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. Three use cases for the ontology are presented with accompanying competency questions, demonstrating the usefulness of the resource. Additionally, this work introduces a novel nanopublications dataset, which serves as a collaborative, living state-of-the-art review of RDF streaming. The results of a multifaceted evaluation of the resource are presented, testing its logical validity, use case coverage, and adherence to the community's best practices, while also comparing it to other works. RDF-STaX is expected to help drive innovation in RDF streaming, by fostering scientific discussion, cooperation, and tool interoperability.

翻译：多年来，研究与实践从多角度探索了RDF流处理，形成了多样化的RDF流定义。由于缺乏统一术语，这种多样性给流处理系统的讨论与集成带来了重大挑战。本研究通过系统化文献中的RDF流类型，构建新型分类体系以填补这一关键研究空白。提出的RDF流分类学（RDF-STaX）以遵循FAIR原则的OWL 2 DL本体形式实现，具备实际可操作性。研究提供了详尽的文档与附加资源以促进本体采纳，并通过三个应用场景及对应能力问题展示了该资源的实用性。此外，本研究创建了新型纳米出版物数据集，作为RDF流处理领域协同演进的最新研究综述。通过多维度评估验证了资源的逻辑有效性、场景覆盖度及对领域最佳实践的遵循性，并与现有工作进行了对比分析。RDF-STaX有望通过促进学术讨论、技术协作与工具互操作性，推动RDF流处理领域的创新发展。