Dialogue systems intend to facilitate the interaction between humans and computers. A key element in a dialogue system is the conceptual model which represents a domain. Folksonomies are very simple forms of knowledge representation which may be used to specify the conceptual model. However, folksonomies suffer by nature from issues related to ambiguity. In this paper, we present a method which uses linguistic context for learning folksonomies from task-oriented dialogues. The linguistic context can be useful for reducing ambiguity, for instance, when using the folksonomies for interpreting utterances. Experiments show that the learned folksonomies increase the accuracy of the interpretation compared when not using the contextual information.