This article discusses the concept of al-Qanun al-Kulliy as a philosophy in under-standing the meaning of the verses of the Qur'an and Hadith of the Prophet (S.A.W), both of which are needed in understanding faith-related issues. The concept here is that sense of purpose, considered priority in outward evidences of Islamic law, which has drawn criticism from Islamic scholars who cling to the methods of the Salaf al-Salih. To understand the concept of al-Qanun al-Kulliy, this paper relies on the analysis of some related sources, the study of which has shown that al-Qanun al-Kulliy is a philosophy in understanding matters of faith that was adopted by some theologians (Ahl al-Kalam). The paper also shows that Ibn Taimiyyah and his student, Ibn al-Qayyim, are among Muslim scholars who maintain firm criticism of al-Qanun al-Kulliy on the premise that it denies many faith-related issues stipulated by the texts of personality (qat'iy). The paper adopts a qualitative approach, being mainly a library-based research study. The aim of the paper is, therefore, to maintain al-Qanun al-Kulliy as a means to understand-ing faith in Islam if properly employed. � Universiti Putra Malaysia Press.

Non-taxonomic relation is one of the most important components in ontology to describe a domain. Currently, most studies focused on extracting non-taxonomic relationships from text within the scope of single sentence. The predicate between two concepts (i.e. subject and object) that appear in a same sentence is extracted as potential relation. Therefore the number of identified relations is less that what it could be and does not properly represent the domain. In this paper, we introduced a method named Domain-specific Inter-textual nontaxonomic extraction (DSINTE) to extract the non-taxonomic relations between two concepts that appear not only in a single sentence but also in different sentences. The proposed method has been illustrated using a collection of domain texts from New York Times website. Recall metrics have been used to evaluate the results of the experiments. � 2015 IEEE.

Extracting non-taxonomic relations is one of the important tasks in the construction of ontology from the text. Most of current methods on identification and extraction of non-taxonomic relations is based on predicate representing relationships between two concepts, namely the relation between subject and object that occurs in a sentence. However, the number of relations that has been identified does not properly represent the domain as the methods only identify a portion of the total relations from domain texts. In this paper, we present a method that increases the number of relations extracted and thus properly represent the domain. In this method, all potential relations are first generated and then less significant ones, based on their frequency, are removed. The method has been tested on a collection of texts that described electronic voting machine and the result is encouraging. � 2011 IEEE.

Existing research focus on extracting the concepts and relations within a single sentence or in subject-object-object pattern. However, a problem arises when either the object or subject of a sentence is "missing" or "uncertain", which will cause the domain texts to be improperly presented as the relationship between concepts is no extracted. This paper proposes a solution for the enrichment of the knowledge of domain text by finding all possible relations. The proposed method suggests the appropriate or the most likely term for an uncertain subject or object of a sentence using the probability theory. In addition, the method can extract the relations between concepts (i.e. subject and object) that appear not only in a single sentence, but also in different sentences by using a synonym of the predicates. The proposed method has been tested and evaluated with a collection of domain texts that describe tourism. Precision, recall, and f-score metrics have been used to evaluate the results of the experiments. � Copyright International Association of Engineers.

Construction of ontology is a difficult task, expensive and time-consuming. Concept, taxonomy and nontaxonomic relations, are the three important components in the development of ontology. These three components are used to represent the whole domain texts. Currently, most of studies focused on extracting the concept, the taxonomic relationships and the non-taxonomic relationships within the scope of single sentence. In order to enrich the domain ontology, we introduced a method to extract the non-taxonomic relations by using the similarities of relations that exist in more than one sentence. The most appropriate predicate are used as a reference to relate between concepts that occur not only in the same sentence, but also in different sentences. Here, the proposed method was tested using a collection of domain texts that described electronic voting machine and are evaluated based on the standard information retrieval performance metrics, i.e. precision and recall. � 2006-2015 Asian Research Publishing Network (ARPN).

Most of the existing techniques on relation extraction focus on extracting relation between subject, predicate and object in a single sentence. However, these techniques unable to handle the situation when the text has sentences that are incomplete: either does not have or unclear subject or object in sentence (i.e. 'unsure' value). Thus this does not properly represent the domain text. This paper proposes an approach to predict and identify the unsure value to complete the sentences in the domain text. The proposed approach is based on the probability theory to identify terms (i.e., subject or object) that are more likely to replace the 'unsure' value. We use voting machine domain text as a case study. � 2015 IEEE.