Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
Knowledge-based intelligent systems might be used in the banking sector to automate customer service. One of the ways to represent knowledge that is both understandable by humans and readable by machines is by using ontologies. Whenever a customer queries its bank regarding specific products or services, the existing knowledge modeled in an ontology might be used by a customer service chatbot to answer it in an automated way. The existing manual information retrieval process from banking specialists is laborious and time-consuming. Specialists use natural language, visual representations, and common sense, often overlooking details. It is a great challenge to make a specialist’s knowledge explicit, formal, precise, and completely scalable, which is the format required by a customer service chatbot. We propose a semi-automatic approach to retrieving banking information in Brazilian Portuguese texts with minimal specialist support. By combining Natural Language Processing techniques (e.g., syntactic analysis to obtain the logical meaning of sentences based on rules and its structure) and an ontology constructor library, it was possible to build a tool that receives texts from the banking domain and constructs an ontology that knowledge-based intelligent systems can use. Specialist support is only needed in intermediate refinement steps, thus optimizing the banking specialist’s time. The use cases for investments, opening a banking account, and the comparison of the proposed approach show how we reduced manual labor in the information retrieval process by a factor of 40%. Our approach can identify more information in each sentence compared to a similar method found in the literature. The resulting ontologies can be used in a chatbot that automates customer support for a large Brazilian bank.
