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Knowledge engineering is one of the key research area to build knowledgebase for providing solutions to real-world problems. Due to rapidly increase of data growth rate, it is almost impossible to extract hidden knowledge with manual approach. Moreover, a number of methodologies have been proposed that focus on some specific aspect of the data mining process rather than end-to-end knowledge engineering methodology. Keeping in view these facts, a Semi-automatic Knowledge Engineering Methodology (SaKEM) is proposed that covers all major stages that are involved in Knowledge Discovery in Databases (KDD) process. For realization of SaKEM, a toolset called Data Driven Knowledge Acquisition Tool (DDKAT) is developed. The proposed methodology is designed for Mining Minds project but it can be utilized by other service-enabled platforms as well.

Item Type:	Conference Extract
Keywords:	features selection, data preprocessing, decision trees, model translation, production rules, knowledge acquisition
Research Division:	Information and Computing Sciences
Research Group:	Distributed computing and systems software
Research Field:	Mobile computing
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Information systems, technologies and services not elsewhere classified
UTAS Author:	Kang, BH (Professor Byeong Kang)
ID Code:	113696
Year Published:	2016
Deposited By:	Engineering
Deposited On:	2017-01-16
Last Modified:	2017-08-31
Downloads:	86 View Download Statistics