Automatic knowledge extraction of any Chatbot from conversation. (15th December 2019)

• Record Type: Journal Article
• Authors: Arsovski, Sasa; Osipyan, Hasmik; Oladele, Muniru Idris; Cheok, Adrian David
• Is Part Of: Expert systems with applications. Volume 137(2019)
• Page Start: 343
• Page End: 348
• Publication Date: 2019-12-15
• Subjects: Human-machine interaction -- Knowledge extraction -- Neural conversational agent -- Neural network -- Rule based chatbot; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.07.014
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Novel methodology for conversational knowledge extraction from the existing chatbot. Building Neural Conversational Agent using a seq2seq-LSTM framework. Big noisy dataset is used as a question base to force conversational knowledge extraction. K-means clustering algorithm is used to define the stopping point for knowledge extraction. Machine-machine conversational knowledge sharing. Abstract: Acquiring knowledge for conversation modeling is an important task in the process of building a Conversational Agent (Chatbot). However, it is a quite difficult process that requires too much time and efforts. To overcome these difficulties, in this paper, we demonstrate a novel methodology for the automatic conversational knowledge extraction from an existing Chatbot. Extracted knowledge will be used as training dataset for building a Neural Network Conversational Agent. The experiments in the paper show that our proposed approach can be used for the automatic knowledge extraction from any type of Chatbot on the Internet. The experiment that is presented in this paper has two phases. In the first phase, we present a methodology for the conversational knowledge extraction. In the second phase of the experiment, we introduce a methodology for building a new Neural Conversational Agent using a deep learning Neural Network framework. The key novelty of our proposed approach is the automated machine-machine conversational knowledge sharing and reuse. This is an important step towards building the new conversational agents skipping the difficult and time-consuming procedure of knowledge acquisition.