Towards an Ontology based integrated Framework for Semantic Web

This Ontologies are widely used as a means for solving the information heterogeneity problems on the web because of their capability to provide explicit meaning to the information. They become an efficient tool for knowledge representation in a structured manner. There is always more than one ontology for the same domain. Furthermore, there is no standard method for building ontologies, and there are many ontology building tools using different ontology languages. Because of these reasons, interoperability between the ontologies is very low. Current ontology tools mostly use functions to build, edit and inference the ontology. Methods for merging heterogeneous domain ontologies are not included in most tools. This paper presents ontology merging methodology for building a single global ontology from heterogeneous eXtensible Markup Language (XML) data sources to capture and maintain all the knowledge which XML data sources can contain

Predicting the Severity of Breast Masses with Data Mining Methods

Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their decisions to perform a breast biopsy on a suspicious lesion seen in a mammogram image or to perform a short term follow-up examination instead. In this research paper data mining classification algorithms; Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) are analyzed on mammographic masses data set. The purpose of this study is to increase the ability of physicians to determine the severity (benign or malignant) of a mammographic mass lesion from BI-RADS attributes and the patient,s age. The whole data set is divided for training the models and test them by the ratio of 70:30% respectively and the performances of classification algorithms are compared through three statistical measures; sensitivity, specificity, and classification accuracy. Accuracy of DT, ANN and SVM are 78.12%, 80.56% and 81.25% of test samples respectively. Our analysis shows that out of these three classification models SVM predicts severity of breast cancer with least error rate and highest accuracy.