Semantic Approach for Al-Quran Knowledge Representation in Islamic Knowledge Management Framework

Using ontology data model to accurately represent Al-Quran knowledge using its inherent structure of Division (Juz), Chapter (Surah), and Verse (Ayah) should be done within the framework of the Islamic Knowledge Management (IKM). Data modelling neglecting the IKM framework would result in inaccuracy and irrelevant result in knowledge processing activities involving the Al-Quran. Many of the current approaches utilizing conventional knowledge management methods such as taxonomy, hierarchy, or tree structure are limited in the sense that they only define the concepts of knowledge without relating it to a theme that defined from corpus of Islamic knowledge. In the case of Al-Quran, a classification of themes from background knowledge are important to provide a structural explanation of the knowledge and to ensure that the results obtained from a searching function of the corpus of the knowledge will result in an accurate and appropriate meaning intended by the Islamic knowledge. The aims of this paper is to demonstrate the construction of Al-Quran ontology and elaborate the searching method for representing and retrieving the relevance and accurate verses toward the theme of Al-Quran by using the semantic-based approach. The specification of Al-Quran ontology of which the prototype application was built on has been validated by experts. The evaluation test of the Al-Quran ontology application furthermore shows high precision result from searching functions done. With a good level of accuracy achieved from the test, it is hopeful that Al-Quran ontology application will provide a new experience of Al-Quran themes searching and browsing within the IKM framework for the Al-Quran readers. A capability to efficiently and accurately performing search of Al-Quran will certainly improve their understanding of Al-Quran contents.

Ontology Application for the Organizational Goals

Data is important in assisting decision-making in relation to the organizational goals. However, the trustworthiness of organizational data in relation to achieving the organizational goals is often questioned because of the vast amount of organizational data available. This paper advances the understanding of the organizational goals model based on ontology. This refers to the importance of assisting the organization to utilize relevant organizational data for decision-making in relation to the organizational goals. Therefore, domain experts and entrepreneurs can make a decision to what extend the organizational goals are achieved. The results show that ontology supports the relationship between the organizational goal elements as an effort to measure organizational data in relation to the organizational goals.

Ontology Application to Construct Inductive Modeling Tools with Intelligent Interface

This paper is devoted to the analysis of sources in the field of development and building intelligent user interfaces. Particular attention is paid to presenting an ontology-based approach to constructing the architecture of the interface, the tasks arising during the development, and ways for solving them. An example of the construction of the intelligent user interface is given for software tools of inductive modeling based on the detailed analysis of knowledge structures in this domain.

A Knowledge Model of Digital Evidence Review Elements Based on Ontology

As existing methods cannot express, share, and reuse the digital evidence review information in a unified manner, a solution of digital evidence review elements knowledge base model based on ontology is presented. Firstly, combing with the multi-source heterogeneous characteristic of digital evidence review knowledge, classification and extraction are accomplished. Secondly, according to the principles of ontology construction, the digital evidence review elements knowledge base model which includes domain ontology, application ontology, and atomic ontology is established. Finally, model can effectively acquire digital evidence review knowledge by analyzing review scenario.