1. Knowledge Representation of Statistic Domain For CBR Application Supervisor : Dr. Aslina Saad Dr. Mashitoh Hashim PM Dr. Nor Hasbiah Ubaidullah

2. What is statistics  Statistics is the mathematical science involved in the application of quantitative principles to the collection, analysis, and presentation of numerical data.  Simply put, statistic is a range of procedures for gathering, organizing, analyzing and presenting quantitative data. (Berman Brown & Saunders, 2007)

3. What is statistics  Statistic can be divided into 2 category:  Descriptive statistics Concerned with quantitative data and the methods for describing them  Inferential statistics Makes inferences about populations by analyzing data gathered from samples and deals with methods that enable a conclusion to be drawn from these data

4. Importance of statistics in research  Statistical methods and analyses are often used to communicate research findings and to support hypotheses and give credibility to research methodology and conclusions.  Some of the major purposes of statistics are to help us understand and describe phenomena in our world and to help us draw reliable conclusions about those phenomena

5. Problem Statement  Research is one of the main activities in the university environment.  It involves many stakeholders including lecturers and students.  Is a major task for Masters and PhD students.  However, the problems encountered is the lack of knowledge among student and lecturer in the field of statistics to carry out research which involves quantitative method.

6.  Most students often have difficulties in performing statistical tests on the data collected.  As a result, they have to consult with experts in the field of statistics to determine the steps that should be taken to analyze their findings.  With this study, hopefully this problem can be solved with the tools that will be developed in order to provide guidance to students in performing statistical studies in accordance with the criteria of their findings.

7. Research Objective  To represent knowledge in statistic domain using OWL  To produce generic model of CBR for statistical test usage in research  To construct knowledge base for statistical test usage in research  To generate ontology mapping to a database (ODBA – Ontology Based Data Integration)  To develop a prototype CBR application for statistical test usage in research  To apply reasoning for the constructed knowledge base via the CBR application

8. Literature Review  Many factors must be considered in determining the statistical tests to be performed on collected data in a study.  It includes types of data, the number of samples, study purposes and many more.  Knowledge of the statistic domain has to be modeled and transformed into some format that works for representing cases which is crucial for the development of a knowledge base for CBR system

9. Semantic Web  This can be represented by using semantic web.  Semantic web is an extension of the current web in which information is given well- defined meaning, better enabling computers and people to work in cooperation. (Berners-Lee et. al., 2009)

10.  Motivation behind the semantic web  Difficult to find, present, access or maintain available electronic information on the web  Need for a data representation to enable software products to provide intelligent access to heterogeneous and distributed information

11. From semantic web to CBR  Main ideas in the semantic web initiative are ontology, standards and layers.  Ontology is a shared conceptualization which expressed in a true knowledge representation language namely OWL  CBR is an AI technique based on reasoning on stored cases  CBR technique can be applied to do intelligent retrieval on metadata related to statistic domain that have been encoded using semantic web

12. Research Methodology

13. The methodology of the study will involve several important phases:  Identification  Knowledge acquisition to understand domain problem  Problem and solution feature definition  Knowledge Analysis  Conceptual Modeling  Knowledge Representation

14.  Construction (Ontology based data Access and CBR application)  Building ontology  Define ontology using OWL  Mapping a database to an ontology  Develop a CBR application  System Implementation and Testing  Implement reasoning  Querying ontology  Test whether the application works

15. Gannt Chart

16. Conclusion  With this research, it is hoped that it will offer invaluable insight and understanding the usage of CBR concept in representing knowledge in statistics that supports semantic web.