Semantic Web is a new revolution in the world of the Web, where information and
data become viable for logical processing by computer programs. Where they are
transformed into meaningful data network. Although Semantic Web is considered the
future
of World Wide Web, the Arabic research and studies are still relatively rare in this
field. Therefore, this paper gives a reference study of Semantic Web and the different
methods to explore the knowledge and discover useful information from the vast amount
of data provided by the web. It gives a programming example like application of some of
these techniques provided by the Semantic Web and methods to discover the knowledge of
it. This simplified programming example provides services related to higher education
Syrian government, such as information about the Syrian public universities like the name
of the university (Syrian Virtual University, Tishreen, Aleppo, Damascus, and Al Baath),
address of the university, its web site, number of students and a summary of the university,
which helps intelligent agents to find those services dynamically.
The Research Aims:
Syrian organizations keep large amounts of information and data about their
personnel in their IT systems. This information, however, is often left unutilized or
may be analyzed through statistical methods. In this study, DM is
considered a
solution for analyzing HR data and explore knowledge from data stored in some
Syrian organization through two major stages:
Stage A: Using results of Semi-Annual performance evaluation process to build
prototype showed in (Fig. 6) to accomplish two tasks:
1. Building a models to predict appropriate job function for an employee
through majority principle and using high accuracy result to increase the
number of training data and make it self-learning model.
2. Choose most important attributes that used in classify methods to use it in
personnel selection and recruitment.
Stage B: Using data of Time & Attendance to analysis personnel activity through
clustering methods and building many meaningful groups.
