We explore data-driven rule aggregation based on latent feature representations in the context of knowledge graph completion. For a given query and a collection of rules obtained by a symbolic rule learning system, we propose end-to-end trainable aggregation functions for combining the rules into a confidence score answering the query. Despite using latent feature representations for rules, the proposed models remain fully interpretable in terms of the underlying symbolic approach. While our models improve the base learner constantly and achieve competitive results on various benchmark knowledge graphs, we outperform current state-of-the-art with respect to a biomedical knowledge graph by a significant margin. We argue that our approach is in particular well suited for link prediction tasks dealing with a large multi-relational knowledge graph with several million triples, while the queries of interest focus on only one specific target relation.