A Conversational Chatbot Based on Kowledge-Graphs for Factoid Medical Questions

In the last years, the interest about enhancing the interface usability of applications has strongly increased, focusing, in particular, on chatbots, i.e. conversational agent that interacts with users, turn by turn using natural language. However, building chatbots for answering to questions over structured medical knowledge bases is a very thorny task and is still considered an open research challenge. In order to face this issue, this paper proposes a knowledge-based conversational chatbot for medical question answering, aimed at supporting: i) the formulation of factoid questions over medical knowledge bases; ii) the generation of more precise and contextualized dialog responses by analyzing the relations between entities in knowledge bases; iii) the detection of ambiguous user intents, with respect to the current dialog state and the suggestion of some interaction hints aimed at clarifying and/or confirming their meaning. A relevant characteristic of this system is represented by the usage of Knowledge Graphs to formally represent textual inputs given by the user as well as templates of questions and, contextually, efficiently navigate and use the domain knowledge of interest to provide an answer. The proposed chatbot has been implemented as a desktop application named “Medical Assistant” able to conversate with users interested to diagnose and identify the possible diseases causing a symptom, and find the most suitable treatment for a medical problem. It has been proficiently tested with respect to some factoid questions, showing its capability to help user reach the desired information also in the case of initial missing information.