Learning Folksonomies from Task-Oriented Dialogues

A dialogue system allows a human to interact with a computer, through the natural language. One of the main components of a dialogue system is the Conceptual Model. The Conceptual Model represents a domain and its specification is given by several forms of knowledge representation. We propose to represent it using folksonomies. We describe a method called FolksDialogue that performs the learning of folksonomies from task-oriented dialogues. In order to check whether the structures created by the method are genuine folksonomies, we performed an experiment to prove that they have the small-world phenomenon, which is a characteristic of folksonomies. The generated folksonomies can be useful in the interpretation of dialogue utterances, indicating whether the utterances belong or not to the domains that the folksonomies represent. The experiments show that the folksonomies learned can perform the interpretation of utterances with an accuracy of 69.20%.