Quran Ontology: Review On Recent Development And Open Research Issues

Quran is the holy book of Muslims that contains the commandment of words of Allah. Quran provides instructions and guidance to humankind in achieving happiness in life in the world and the hereafter. As a holy book, Quran contains rich knowledge and scientific facts. However, humans have difficulty in understanding the Quran content. It is caused by the fact that the meaning of the searched message content depends on the interpretation. Ontology able to store the knowledge representation of Holy Quran. This paper studies recent ontology on Holy Quran research. We investigate the current trends and technology being applied. This investigation cover on several aspects, such as outcomes of previous studies, language which used on ontology development, coverage area of Quran ontology, datasets, tools to perform ontology development ontology population techniques, approaches used to integrate the knowledge of Quran and other resources into ontology, ontology testing techniques, and limitations on previous research. This review has identified four major issues involved in Quran ontology, i.e. availability of Quran ontology in various translation, ontology resources, automated process of Meronymy relationship extraction, and Instances Classification. The review of existing studies will allow future researchers to have a broad and useful background knowledge on primary and essential aspects of this research field

A General Architecture to Enhance Wiki Systems with Natural Language Processing Techniques

Wikis are web-based software applications that allow users to collaboratively create and edit web page content, through a Web browser using a simplified syntax. The ease-of-use and “open” philosophy of wikis has brought them to the attention of organizations and online communities, leading to a wide-spread adoption as a simple and “quick” way of collaborative knowledge management. However, these characteristics of wiki systems can act as a double-edged sword: When wiki content is not properly structured, it can turn into a “tangle of links”, making navigation, organization and content retrieval difficult for their end-users. Since wiki content is mostly written in unstructured natural language, we believe that existing state-of-the-art techniques from the Natural Language Processing (NLP) and Semantic Computing domains can help mitigating these common problems when using wikis and improve their users’ experience by introducing new features. The challenge, however, is to find a solution for integrating novel semantic analysis algorithms into the multitude of existing wiki systems, without the need for modifying their engines. In this research work, we present a general architecture that allows wiki systems to benefit from NLP services made available through the Semantic Assistants framework – a service-oriented architecture for brokering NLP pipelines as web services. Our main contributions in this thesis include an analysis of wiki engines, the development of collaboration patterns be- tween wikis and NLP, and the design of a cohesive integration architecture. As a concrete application, we deployed our integration to MediaWiki – the powerful wiki engine behind Wikipedia – to prove its practicability. Finally, we evaluate the usability and efficiency of our integration through a number of user studies we performed in real-world projects from various domains, including cultural heritage data management, software requirements engineering, and biomedical literature curation