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Automatic knowledge extraction of any Chatbot from conversation
- Source :
- Expert Systems with Applications. 137:343-348
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
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.
- Subjects :
- 0209 industrial biotechnology
Computer science
media_common.quotation_subject
02 engineering and technology
computer.software_genre
Chatbot
020901 industrial engineering & automation
Knowledge extraction
Artificial Intelligence
Human–computer interaction
0202 electrical engineering, electronic engineering, information engineering
Conversation
Dialog system
media_common
business.industry
Deep learning
General Engineering
Novelty
Knowledge acquisition
Computer Science Applications
Knowledge sharing
020201 artificial intelligence & image processing
The Internet
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 137
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........7a25ed3089875b4e4c15c06bfe86b2cb