Concepts extraction in ontology learning using language patterns for better accuracy

Rohana Ismail, Nurazzah Abd Rahman, Z. Bakar, M. Makhtar
{"title":"Concepts extraction in ontology learning using language patterns for better accuracy","authors":"Rohana Ismail, Nurazzah Abd Rahman, Z. Bakar, M. Makhtar","doi":"10.1109/CATA.2018.8398668","DOIUrl":null,"url":null,"abstract":"The identification of concepts and relations via automatic or semiautomatic are tasks in Ontology Learning. The Ontology Learning is important in minimizing effort of ontology development. It has been used in many disciplines including development of Quran ontology. In the Quran ontology development, there have been efforts to identify concepts and relations for ontology development using various methods. Among the methods employed to discover concepts is a regex pattern. The pattern is based on NLP which use tagging in their rules. This paper proposed a method that used patterns to extract concepts for Hajj Ontology development. It also has been compared against a prominence Ontology Learning system i.e. Text2Onto. The patterns also have been compared with Qterm pattern which is specifically designed for Solah domain in the Quran. Results indicate that the proposed patterns improve the precision with 82.4% and recall with 85.7% as compared to the both approaches.","PeriodicalId":231024,"journal":{"name":"2018 4th International Conference on Computer and Technology Applications (ICCTA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 4th International Conference on Computer and Technology Applications (ICCTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CATA.2018.8398668","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

The identification of concepts and relations via automatic or semiautomatic are tasks in Ontology Learning. The Ontology Learning is important in minimizing effort of ontology development. It has been used in many disciplines including development of Quran ontology. In the Quran ontology development, there have been efforts to identify concepts and relations for ontology development using various methods. Among the methods employed to discover concepts is a regex pattern. The pattern is based on NLP which use tagging in their rules. This paper proposed a method that used patterns to extract concepts for Hajj Ontology development. It also has been compared against a prominence Ontology Learning system i.e. Text2Onto. The patterns also have been compared with Qterm pattern which is specifically designed for Solah domain in the Quran. Results indicate that the proposed patterns improve the precision with 82.4% and recall with 85.7% as compared to the both approaches.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
在本体学习中使用语言模式进行概念抽取,提高准确性
通过自动或半自动的方式识别概念和关系是本体学习中的任务。本体学习对于最小化本体开发工作具有重要意义。它已被用于许多学科,包括古兰经本体的发展。在《古兰经》本体发展中,人们一直在努力用各种方法来确定本体发展的概念和关系。用于发现概念的方法之一是正则表达式模式。该模式基于NLP,在其规则中使用标记。本文提出了一种利用模式抽取概念的方法,用于朝觐本体的开发。它还与一个突出的本体学习系统Text2Onto进行了比较。这些模式还与《古兰经》中专门为太阳神领域设计的Qterm模式进行了比较。结果表明,与两种方法相比,该方法的查准率提高了82.4%,查全率提高了85.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Classification of pornographic content on Twitter using support vector machine and Naive Bayes Concepts extraction in ontology learning using language patterns for better accuracy Automatically generate a specific human computer interaction from an interface diagram model The Triple Helix Model: University-industry-governments linkage web-based application recommendation systems for emerging commercial-base research State of the art of telepresence with a virtual reality headset
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1