The Chatbot Systems for Mathematics Education using Knowledge Graphs and Named Entity Recognition

Abstract

The development of chatbot systems for mathematics courses in elementary school has gained significant attention due to their potential to enhance the learning experience. This study proposes a novel approach that combines knowledge graph (KG) and Named Entity Recognition (NER) methods using Neo4j and SpaCy within the Rasa Open-Source v3.0 platform as chatbot frameworks. The knowledge graphs represent mathematical concepts and their relationships, enabling the chatbot to provide accurate and relevant responses to user queries. The NER SpaCy is employed to identify and extract mathematical entities from user inputs, ensuring a precise understanding of the context. The integrations of Neo4j and NER using SpaCy with Rasa Open-Source v3.0 facilitate efficient information retrieval and improve the conversational abilities of the chatbot. Experimental results demonstrate the effectiveness of the proposed approach, showcasing its potential as an educational tool for fifth-grade students in elementary schools.