Subject headings systems are tools for organization of knowledge that have been developed over the years by libraries. The SKOS Simple Knowledge Organization System provides a practical way to represent subject headings systems, and several libraries have taken the initiative to make these systems widely available as open linked data. Each individual subject heading describes a concept, however, in the majority of cases, one subject heading is actually a combination of several concepts, such as a topic bounded in geographical and temporal scopes. In these cases, the label of the concept actually contains several concepts which are not represented in structured form. This paper address the alignment of the geographic concepts described in subject headings systems with their correspondence in geographic ontologies. Our approach first recognizes the place names in the subject headings using entity recognition techniques and follows with the resolution of the place names in a target geographic ontology. The system is based on machine learning and was designed to be language independent so that it can be applied to the many existing subject headings systems. Our approach was evaluated on a subset of the Library of Congress Subject Headings, achieving an F1 score of 93%.