Comparison of Ontology Learning Techniques for Qur'anic Text

Abstract

Currently, ontology plays an important role in semantic web technology. Ontology learning approach is to distinguish the type of input such as text, dictionary, knowledge, policies, schemes and semi-structured schemes relations. Ontology learning can be explained as information extraction subtask and its objectives are to dig the relevant concepts and relationships from the corpus or a particular type of data sets. In this project, an ontology learning of text extraction from Qur'anic text as input data was assessed using a newly developed support system. The algorithms used to extract Qur'anic text in this project are Alfonseca & Manandhar's and Gupta & Colleagues's approach. The support system will assess and evaluate these two algorithms and compare with the manually text extraction (Gold Standard) in order to come out an appropriate method or technique which suitable to extract the ontologies from Qur'anic text which can help more people to understand the true meaning from Qur'an teaching.