abstract
- © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.The scope of this research work is to integrate a statistical ontology model of scientometric indicators in a chatbot. Building a chatbot requires the use of Natural Language Processing (NLP) as a capability for recognizing users¿ intent and extracting entities from users¿ questions. We proposed a method for recognizing the requested indicator and transforming the question expressed in natural language into a query to the semantic model. The chatbot and the ontology model represent a novel framework that can answer questions about Scientometric Indicators. The chatbot is evaluated in terms of Goal Completion Rate (GCR). It measures how many questions the chatbot answered correctly and identifies intent and entity extraction correctly. The second evaluation approach of the chatbot is a survey that focuses on usability, the strictness of language variations, chatbot comprehension, correlation in chatbot responses, and user satisfaction.