For biodiversity research, the field of study that is concerned with the richness of species of our planet, it is of the utmost importance that the location of an animal specimen find is known with high precision. Due to specimens often having been collected over the course of many years, their accompanying geographical data is often ambiguous or may be very imprecise. In this article, we detail an approach that utilizes reasoning and external sources to improve the geographical information of animal finds. Our main contribution is to show that adding external domain knowledge improves the ability to georeference locations over traditional methods that focus solely on analyzing geographical information. Additionally, our system is able to output the confidence it has in its decisions through a confidence measure based on the difficulty of the instance and the steps undertaken to disambiguate it. Our results show that adding domain knowledge to the georeferencing process increases the accuracy @5km from 38.9% to 61.7% and from 47.0% to 74.5% @25km. Furthermore, we reduce the mean distance by more than half, from 251.1km to 114.5km, and decrease the number of records for which no reference can be found from 26.2% to 7.4%.