To facilitate interaction with mobile health applications, chatbots are increasingly used. They realize the interaction as a dialog where users can ask questions and get answers from the chatbot. A big challenge is to create a comprehensive knowledge base comprising patterns and rules for representing possible user queries the chatbot has to understand and interpret. In this work, we assess how crowdsourcing can be used for generating examples of possible user queries for a medication chatbot. Within one week, the crowdworker generated 2'738 user questions. The examples provide a large variety of possible formulations and information needs. As a next step, these examples for user queries will be used to train our medication chatbot.
Keywords: Conversational user interface; artificial intelligence; chatbot; crowdsourcing; medication management; natural language understanding.