Many applications require the processing of event streams from different sources in combination with large amounts of background knowledge. Semantic CEP is a paradigm explicitly designed for that. It extends complex event processing (CEP) with RDF support and uses a network of operators to process RDF streams combined with RDF knowledge bases. Another popular class of systems designed for a similar purpose is the RDF stream processors (RSPs). These are systems that extend SPARQL (the RDF query language) with stream processing capabilities. Semantic CEP and RSPs have similar purposes but focus on different things. The former focuses on scalability and distributed processing, while the latter tends to focus on the intricacies of RDF stream processing per se. In this thesis, we propose the use of RSP engines as building blocks for Semantic CEP. We present an infrastructure, called DSCEP, that allows the encapsulation of existing RSP engines into CEP-like operators so that these can be seamlessly interconnected in a distributed, decentralized operator network. DSCEP handles the hurdles of such interconnection, such as reliable communication, stream aggregation and slicing, event identification and time-stamping, etc., allowing users to concentrate on the queries. We also discuss how DSCEP can be used to speed up monolithic SPARQL queries; by splitting them into parallel subqueries that can be executed by the operator network or even by splitting the input stream into multiple operators with the same query running in parallel. Additionally, we evaluate the impact of the knowledge base on the processing time of SPARQL continuous queries.