SEMANTIC WEB - CREATION OF TAXONOMY FOR A COLLECTION OF JOURNAL ARTICLES

The whole idea of World Wide Web (WWVV) was born out of the desire that information should be shared. Presently WWW is really information domain. Today, users of the Web can easily access this information by specifying URI addresses, searching, and following links to find other related resources, but, in some cases users are taken to an irrelevant site. Another limitation of WWW is that the information can only be understood and interpreted by human being, it is difficult for machine to understand the information therein. There is need for computer to talk to themselves, understand the data on the web and make intelligent decision on the data. The goal of the Semantic Web is to develop enabling standards and technologies designed to receive more exact results when searching for information, and to help machines understand more information on the Web so that · they can support richer discovery, data integration and navigation. The study overviewed the whole essence of semantic web in relation to the traditional web as an introduction to the study. Some literatures related to semantic web were reviewed. 100 journal articles were downloaded from the internet which were used as sample data. These journal articles were serialized, stemmed and tokenized. Term frequency was calculated for each journal article. Some representative terms were selected from each journal article and similarity matrix was generated for the entire journal article. Complete hierarchical clustering was used to create a cluster of the articles. Java Tree view program was used to view the dendrogram of the cluster. It was observed that the articles cluster around their subject, subject area, field of study, area of application, journal type, author, place of case study and that journal articles have properties on which could be created taxonomy which ts the basis for semantic web.