Concepts extraction in ontology learning using language patterns for better accuracy

Rohana Ismail, Nurazzah Abd Rahman, Z. Bakar, M. Makhtar
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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.
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。
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