Nowadays, high volumes of data are generated and published at a very high velocity by real-time systems, such as social networks, e-commerce, weather stations and sensors, producing heterogeneous data streams. To take advantage of linked data and offer interoperable solutions, semantic Web technologies have been used. To analyze these huge volumes of data, different stream mining algorithms exist such as compression or loadshedding. Nevertheless, most of them need many passes through the data and often store part of it on disk. If we want to apply efficient compression on semantic data streams, we need to first detect frequent graph patterns in RDF streams. In this article, we present FreGraPaD, an algorithm that detects those patterns in a single pass, using exclusively internal memory and following a data structure oriented approach. Experimental results clearly confirm the good accuracy of FreGraPaD in detecting frequent graph patterns from semantic data streams