An Efficient Search for Context-Based Chatbots

Abstract

A chatbot is one of the Artificial Intelligence applications which has a capability of making human users to feel real and understood in real time. Using chatbot in any business process may increase customers' satisfaction as it is available 24/7 to entertain customers' needs. A chatbot is expected to be more efficient than a human personnel in responding to customers' questions. Thus, having a chatbot that is able to retrieve answers for all questions in a quick and efficient way is very desirable. The aim of this paper is to introduce a new method called B-Point Tree to increase the efficiency of searching for accurate answers by adding an additional data structure to the traditional BST algorithm. The performance of the B-Point Tree has been compared to the traditional BST, results of experiments suggest that B-Point Tree outperforms the traditional BST. The findings suggest that the B-Point Tree is able to enhance the efficiency of a chatbot.