Semantic Ontology Speech Information Extraction using Non-parametric Correlation Coefficient

Abstract

Abstract On retrieving high frequency keywords in information retrieval system, mismatchings to user's request are problems because of the various meanings of keywords in the existing ontology configuration. In this paper, it is to construct personnel selection ontology and rules in personnel management which are composed of various concepts and knowledges based on semantic web technology and suggest selection procedures to support these rules and knowledge retrieval system to verify suitability of selection results. This system utilizes a method of extraction of speech features by using non-parametric correlation coefficient. This proposed method has been validated by showing that the result average SNR of the experiment evaluation of the proposed techniques was shown to be decreased by .752dB. Key Words : ontology, semantic web, non-parametric correlation coefficient * 이 논문은 2013년도 가천대학교의 교내연구비 지원에 의한 결과임.GCU-2013-R188)Received 8 July 2013, Revised 2 August 2013Accepted 20 September 2013Corresponding Author: Byungwook Lee(The Society of Digital Policy) Email: leebw@gachon.ac.krⒸ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.otg/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN: 1738-1916 properly cited.