While most previous work on Wikification has focused on written texts, this paper presents a Wikification approach for spoken dialogues. A set of analyzers are proposed to learn dialogue-specific properties
along with domain knowledge of conversations from Wikipedia. Then, the analyzed properties are used as constraints for generating candidates, and the candidates are ranked to find the appropriate links. The experimental results show that our proposed approach can significantly improve the performances of the task in human-human dialogues.