Revising Islamic Studies Courses for Improving Cordial Interfaith Relations in Nigeria: A Case Study of Umaru Musa Yar’adua University Katsina, Nigeria

Nigeria has been, for the last four decades, struggling with the menace of inter-religious hostilities between Christians and Muslims who formed the largest religious groups in the country. Numerous policies and programs brokered by various Governments and non-Governmental organizations to curtail the situation failed to yield the desired result. Islamic studies as one of the widely offered programs in the Nigerian universities has the prospect of offering solution to the predicament. However, the courses taught in the program are mainly studies on the Qur’an, Hadith, Tauhid, Ibadat, Fiqh, Islamic civilization, thought and history without single course on interfaith relations. Taking Umaru Musa Yar’adua University Katsina (UMYUK)-Nigeria, as a study case, this paper attempts to draft and propose the inclusion of interfaith relations courses in the curriculum of Islamic Studies programs at the university level in Nigeria for realization of peaceful coexistence in the country. The researcher uses primary data from the Qur’an and sunnah as well as secondary data from different sources. The paper employs exegetical methods and adopts content analysis in the process of conducting the research. The article recommends merging of duplicated courses in the existing curriculum and inclusion of the proposed courses by the Nigerian universities and other institutions of higher learning that offer various Islamic studies programs for the attainment of peaceful interfaith relations in the country.

A Bayesian Network and Ontology Based Semantic Annotation Framework

The internet has become an attractive avenue for global e-business, e-learning, knowledge sharing, etc. Due to continuous increase in the volume of web content, however, it is not practically possible for a user to extract information by browsing and integrating data from a huge amount of web sources retrieved by the existing search engines. The semantic web technology aims to answer this and many other information extraction related issues by providing a suite of tools for integrating data from different sources. To take full advantage of semantic web, however, it is necessary to annotate existing web pages with semantics. Another difficulty that logically arises while accessing information over the web is the presence of unstructured, ungrammatical and incoherent format such as online advertisements, emails, reports etc. This thesis aims to answer few of the concern raised above and presents a semantic annotation framework that is capable of extracting relevant data from unstructured, ungrammatical and incoherent data sources and semantically annotating it. The semantic annotation framework is named BNOSA and it employs ontology and Bayesian network to perform semantic annotation. As the data is unstructured and ungrammatical, the framework exploits the use of context keywords along with domain knowledge to find the location of the data of interest in relevant data sources. Due to the variable size of information available on different web pages, it is often the case that the extracted data contains missing values for certain variables of interest or it may extract more than one value (conflicting values). It is desirable in such situations to predict the missing values and to resolve the conflicts by selecting the most relevant value. BNOSA employs Bayesian networks for missing value prediction and conflict resolution. The framework is extensible as it is capable of dynamically linking any problem domain given a pre-defined ontology and a corresponding Bayesian network. Experiments have been conducted to analyze the performance of BNOSA on several problem domains. The sets of corpora used in the experiments belong to selling-purchasing websites where product information is entered by ordinary web users in a structure free format. The results show that BNOSA performs better than the other recently proposed semantic annotation frameworks.