Abstract : The pub/sub communication style is a prevalent messaging pattern for filtering information from distributed and large-scale network (e.g., from the real-time web, sensor networks, etc.) thanks to the decou- pling between publishers and subscribers. At the same time, persisting the published information is a prerequisite for any further batch analyt- ics on such big amount of data. As data can be heterogeneous, reliance on format from the semantic web such as RDF is unavoidable. In this paper we introduce two versions of a content-based pub/sub matching algorithm for RDF described events, working on an adapted version of the CAN structured P2P network designed to both store and dissemi- nate RDF events. In contrary to existing pub/sub solutions based upon structured overlay networks that index semantic events several times due to the use of hash functions, we leverage the lexicographic order of the event elements. Thus, only subscriptions and not publications have to be duplicated, which is better given that in real settings, publications may occur more frequently than subscriptions. Furthermore, our system al- lows to publish events made of any number of elements and the subscrip- tion language leverages the SPARQL query language. The first algorithm we introduce initially derives from the ideas discussed by Liarou. et al. based upon rewriting continuous queries along matching RDF elements (CSBV) with the purpose to perform the matching between subscriptions and several RDF elements on multiple nodes. The experimental results discuss the applicability of the presented algorithms to some synthetic scenarios and identify, accordingly, which pub/sub matching algorithm is the more relevant.