Knowledge Representation of Statistic Domain For CBR Application

Supervisor :Dr. Aslina Saad

Dr. Mashitoh HashimPM Dr. Nor Hasbiah Ubaidullah

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)

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

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

Problem StatementResearch 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.

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.

Research ObjectiveTo represent knowledge in statistic domain

using OWLTo produce generic model of CBR for statistical

test usage in researchTo 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

Literature ReviewMany 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

Semantic WebThis 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)

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

From semantic web to CBRMain 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

Research Methodology

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

 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

Gannt Chart

ConclusionWith 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.