A Conversational Framework for Semantic Question Answering in Customer Services with Machine Learning on Knowledge Graph

Despite the recent advances in Natural Language Processing (NLP) techniques many issues and inefficiencies arise when it comes to creating a system capable of interacting with the users by means of text conversations. The current techniques rely on the development of chatbots that, however, require designing the conversation flow, defining training questions and associating the expected responses. Even though this process allows the creation of effective question-answering systems, this methodology is not scalable, especially when the answers are to be found in documents. Other approaches, instead, rely on graph embedding techniques and graph neural networks to define the best answer given a question. These methods, however, require set-up training routines and to dispose of the ground truth that, in general, is difficult to retrieve or create for real industrial applications. In this paper we introduce a conversational framework for semantic question answering. Our work relies on knowledge graphs and the use of machine learning for determining the best answer given a question associated with the content of the knowledge graph. In addition, by leveraging text mining techniques we are able to identify the best set of answers that suit the question that are further filtered by means of deep learning algorithms.