Ontology mapping is a crucial step for the facilitation of information exchange between knowledge sources. In the industry this process is often performed semi-automatically, with a domain expert supervising the process. Such an expert can supply a partial alignment, known as anchors, which can be exploited with more elaborate mapping techniques in order to identify the remaining correspondences. To do this we propose a novel approach, referred to as anchor-profiles. For each concept its degree of similarity to each anchor is gathered into a profile for comparison. We evaluated our approach on the Ontology Alignment Evaluation Initiative (OAEI) benchmark dataset using partial alignments that are randomly generated from the reference alignments. The evaluation reveals an overall high performance when compared with mapping systems that participated in the OAEI2012 campaign, where larger partial alignments lead to a higher f-measure.