A Holistic View over Ontologies for Streaming Linked Data

Applied research and prototypes constitute an important part of the initiative around Stream Reasoning (SR) research. From Social Media analytics to the monitoring of IoT streams, the SR community worked hard on designing working prototypes, query languages, and benchmarks. Applied work that uses stream reasoners in practice often requires a data modeling effort. For this purpose, RDF Stream Processing (RSP) engines often rely on OWL 2 ontologies. Although the literature on Knowledge Representation (KR) of Time-varying data is extensive, a survey investigating KR for Streaming Linked Data is still missing. In this paper, we describe an overview of the most prominent ontologies used within RSP applications and compare their data modeling and KR capabilities for Streaming Linked Data. We discuss these ontologies using three complementary KR views, i.e. viewing the streams as Web resources, a view on the structure of the stream, and a view on the modeling of the events in the streams themselves. For each view, we propose an analysis framework to facilitate fair comparison and in-depth analysis of the survey ontologies.