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

Structured and unstructured data integration with electronic medical records

In recent years there has been a great population and technological evolution all over the world. At the same time, more areas beyond technology and information technology have also developed, namely medicine, which has led to an increase in average life expectancy which in turn, leads to a greater need for healthcare. In order to provide the best possible treatments and healthcare services, nowadays the hospitals store large amounts of data regarding patients and diseases (in the form of electronic medical records) or the logistics of some departments in their storage systems. Therefore, computer science techniques such as data mining and natural language processing have been used to extract knowledge and value from these information-rich sources in order not only to develop, for example, new models for disease prediction, as well as improving existing processes in healthcare centres and hospitals. This data storage can be done in one of three ways: structured, unstructured or semi-structured. In this paper, the author tested the integration of structured and unstructured data from two different departments of the same Portuguese hospital, in order to extract knowledge and improve hospital processes. Aiming to reduce the value loss of loading data that is not used in the healthcare providers systems.Nos últimos anos tem-se assistido a uma grande evolução populacional e tecnológica por todo o mundo. Paralelamente, mais áreas para além da tecnologia e informática têm-se também desenvolvido, nomeadamente a área da medicina, o que tem permitido um aumento na esperança média de vida que por sua vez leva a uma maior necessidade de cuidados de saúde. Com o intuito de fornecer os melhores serviços de saúde possíveis, nos dias que hoje os hospitais guardam nos seus sistemas informáticos grandes quantidades de dados relativamente aos pacientes e doenças (sobre a forma de registos médicos eletrónicos) ou relativos à logística de alguns departamentos dos hospitais, etc. Por conseguinte, a estes dados têm vindo a ser utilizadas técnicas da área das ciências da computação como o data mining e o processamento da língua natural para extrair conhecimento e valor dessas fontes ricas em informação com o intuito não só de desenvolver, por exemplo, novos modelos de predição de doenças, como também de melhorar processos já existentes em centros de saúde e hospitais. Este armazenamento de dados pode ser feito em uma de três formas: de forma estruturada, não estruturada ou semi-estruturada. Neste trabalho o autor testou a integração de dados estruturados e não estruturados de dois departamentos diferentes do mesmo hospital português, com o intuito de extrair conhecimento e melhorar os processos do hospital. Com o intuito de reduzir a perda do armazenamento de dados que não são utilizados

Pragmatism and methodology: doing research that matters with mixed methods

DescriptionContentsResourcesCoursesAbout the Authors Taking a pragmatist approach to methods and methodology that fosters meaningful, impactful, and ethical research, this book rises to the challenge of today's data revolution. It shows how pragmatism can turn challenges, such as the abundance and accumulation of big qualitative data, into opportunities. The authors summarize the pragmatist approach to different aspects of research, from epistemology, theory, and questions to ethics, as well as data collection and analysis. The chapters outline and document a new type of mixed methods design called 'multi-resolution research,” which serves to overcome old divides between quantitative and qualitative methods. It is the ideal resource for students and researchers within the social and behavioural sciences seeking new ways to analyze large sets of qualitative data. This book is also available as Open Access on Cambridge Core