Open AccessInformation Retrieval Based on Brazilian Portuguese Texts
Author(s) -
Victor Takashi Hayashi,
Mateus Carvalho,
João Carlos de Lima Neto,
Felipe Coelho de Abreu Pinna,
Rosangela de Fátima Pereira Marquesone,
Wilson Vicente Ruggiero,
Maisa Duarte
Publication year - 2022
Publication title -
journal of systemics, cybernetics, and informatics/journal of systemics cybernetics and informatics
Language(s) - English
Resource type - Journals
eISSN - 1690-4524
pISSN - 1690-4532
DOI - 10.54808/jsci.20.01.249
Subject(s) - computer science , chatbot , ontology , service (business) , sentence , natural language , information retrieval , natural language processing , process (computing) , world wide web , knowledge management , artificial intelligence , data science , programming language , philosophy , economy , epistemology , economics
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.