Semantic similarity measurement is a key methodology in various domains ranging from cognitive science to geographic information retrieval on the Web. Meaningful notions of similarity, however, cannot be determined without taking additional contextual information into account. One way to make similarity measures context-aware is by introducing weights for specific characteristics. Existing approaches to automatically determine such weights are rather limited or require application specific adjustments. In the past, the possibility to tweak similarity theories until they fit a specific use case has been one of the major criticisms for their evaluation. In this work, we propose a novel approach to semi-automatically adapt similarity theories to the user’s needs and hence make them context-aware. Our methodology is inspired by the process of georeferencing images in which known control points between the image and geographic space are used to compute a suitable transformation. We propose to semi-automatically calibrate weights to compute inter-instance and inter-concept similarities by allowing the user to adjust pre-computed similarity rankings. These known control similarities are then used to reference other similarity values.