This is code base of the Final Thesis I defended in 2019 to conclude my M.Sc. Computer Science and Engingeering at Politecnico di Milano.
Title: A conceptual modeling approach for the rapid development of chatbots for conversational data exploration
Url: https://www.politesi.polimi.it/handle/10589/147420
Abstract: A chatbot is a a conversational software agent able to interact with user through natural language, using voice or text channels and trying to emulate human-to-human communication. This Thesis presents a framework for the design of Chatbots for Data Exploration. With respect to conversational virtual assistants (such as Amazon Alexa or Apple Siri), this class of chatbots exploits structured input to retrieve data from known data sources. The approach is based on a conceptual representation of the available data sources, and on a set of modeling abstractions that allow designers to characterize the role that key data elements play in the user requests to be handled. Starting from the resulting specifications, the framework then generates a conversation for exploring the content exposed by the considered data sources. The contributes of this Thesis are: a characterization of Chatbots for Data Exploration, in terms of requirements and goals that must be satisfied when designing these agents; a methodology to design chatbots which poses the attention on some notable data elements, along with a technique to generate conversational agents based on annotations made directly on the schema of the data source; an architecture for a framework supporting the design, generation and execution of these chatbots exploiting state-of-art technologies; a prototype of the framework integrating different technologies for chatbot deployment. The Thesis also reports on some lessons learned that we derived from a user study that compared the user performance and satisfaction with a chatbot generated with our framework to the performance and satisfaction of the same users when interacting the basic SQL command line.