Streaming Linked Data represents a domain within the Semantic Web dedicated to incorporating Stream Reasoning capabilities into the Semantic Web stack to address dynamic data challenges. Such applied endeavours typically necessitate a robust data modelling process. To this end, RDF Stream Processing (RSP) engines frequently utilize OWL 2 ontologies to facilitate this requirement. Despite the rich body of research on Knowledge Representation (KR), even concerning time-sensitive data, a notable gap exists in the literature regarding a comprehensive survey on KR techniques tailored for Streaming Linked Data. This paper critically overviews the key ontologies employed in RSP applications, evaluating their data modelling and KR abilities specifically for Streaming Linked Data contexts. We analyze these ontologies through three distinct KR perspectives: the conceptualization of streams as Web resources, the structural organization of data streams, and the event modelling within the streams. An analytical framework is introduced for each perspective to ensure a thorough and equitable comparison and deepen the understanding of the surveyed ontologies.