With the growing availability of semantic datasets, the processing of such datasets becomes the focus of interest. In this paper, we introduce a new architecture that supports the aggregation of different types of semantic data and provides components for deriving recommendations and predicting relevant relationships between dataset entities. The developed system supports different types of data sources (e.g. databases, semantic networks) and enables the efficient processing of large semantic datasets with several different semantic relationship types. We discuss the presented architecture and describe an implemented application for the entertainment domain. Our evaluation shows that the architecture provides a powerful and flexible basis for building personalized semantic recommender systems.