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The integration of distributed information sources is a key challenge in data and knowledge management applications. Instances of this problem range from mapping schemas of heterogeneous databases to object reconciliation in linked open data repositories. In this paper, we approach the problem of aligning description logic ontologies. We focus particularly on the problem of computing coherent alignments, that is, alignments that do not lead to unsatisfiable classes in the resulting merged ontologies. We believe that considering coherence during the alignment process is important as it is this logical concept that distinguishes ontology alignment from other data integration problems. Depending on the heterogeneity of the ontologies it is often more reasonable to generate alignments with at most k correspondences because not every entity has a matchable counterpart. We describe both greedy and optimal algorithms for computing coherent top-k alignments between OWL EL ontologies and assess their performance relative to state-of-the-art matching systems.