The success of machine represented web known as semantic web largely hinges on ontologies. Ontology is a data modeling technique for structured data repository premised on collection of concepts with their semantic relationships and constraints on domain. There are existing methodologies to aid ontology development process. However, there is no single correct ontology design methodology. Therefore, this paper aims to present a review on existing ontology development approaches for different domains with the goal of identifying individual methodology’s weakness and suggests for hybridization in order to strengthen ontology development in terms of its content and constructions correctness. The analysis and comparison of the review were carried out by considering these criteria but not limited to: activities of each method, the initial domain of the methodology, ontology created from scratch or reuse, frequently used ontology management tools based on literature, subject granularity, and usage across different platforms. This review based on the literature showed some approaches that exhibit the required principles of ontology engineering in tandem with software development principles. Nonetheless, the review still noted some gaps among the methodologies that when bridged or hybridized a better correctness of ontology development would be achieved in building intelligent system.

The systems related to safety are becoming more and more important and are dependent on complex data both in terms of volume and variety. This is especially of importance in applications demanding data analysis, intensive maintenance and focuses on the potential threats due to possible data errors, such as railway signaling, traffic management etc. Errors in analysis of data could result in loss of many lives and financial loss such as the cases of Annabella container ship- Baltic Sea accident (United Kingdom Merchant Shipping, Regulations 2005 – Regulation 5). Despite these potential errors in data leading to accidents or mishaps, this part of the system has been ignored; this study focuses on the integrity of data in safety critical applications. It did so by developing a method for building metadata through a data chain, mining this metadata and representing it in such a way that a consumer of the data can judge the integrity of the data and factor this into the decision-making aspect of their response. This research proposes a design, implementation and evaluation of a safety data model that helps to ensure integrity of data use for data analysis and decision making to prevent loss of lives and properties. Modern and sophisticated ETL software tools including Microsoft SQL Server 2012 Data Tools and Microsoft SQL Server Management Studio were explored. The data were extracted from Safety Related Condition Reports (SRCRs) dataset and used data mining techniques to transform and filter unsafe and hazardous data from the extracted data and stored the safe data into the Data Warehouses (DWs). The prototype was able to load data into designated DWs. The success of the developed model proved that the prototype was able to extract all datasets, transform and load data into the DWs and moved extracted files to archive folder within 7.406 seconds.

High costs of medical equipment and insufficient number of medical specialists have immensely contributed to the increment of death rate especially in rural areas of most developing countries. According to Roll Back Malaria there are 300 million acute cases of malaria per year worldwide, causing more than one million deaths. About 90% of these deaths happen in Africa, majorly in young children. Besides malaria when tested; a large number is coinfected with typhoid. Most often, symptoms of malaria and typhoid fevers do have common characteristics and clinicians do have difficulties in distinguishing them. For instance in Nigeria the existing diagnostic systems for malaria and typhoid in rural settlements are inefficient thereby making the result to be inaccurate and resulting to treatment of wrong ailments. Therefore in this paper, a predictive symptoms-based system for malaria and typhoid coinfection using Support Vector Machines (SVMs) is proposed for an improved classification results and the system is implemented using Microsoft Visual Basic 2013. Relatively high performance accuracy was achieved when tested on a reserved data set collected from a hospital. Hence the system will be of a great significant use in terms of affordable and quality health care services especially in rural settlement as an alternative and a reliable diagnostic system for the ailments.