Personalize Web Searching Strategies Classification and Comparison

Personalization is becoming very important direction in semantic web search for the users that needs to find appropriate information. In this paper, a classification of web personalization is proposed and semantic web search tools are investigated. Building user interest profile is essential for personalizing. Nowadays, semantic web tools use ontologies for personalization because of their advantages. It is important to mention that most of the semantic web search tools use agent technologies for implementation.

Semantic system for searching of employees

Many people have stress to leave their job and start a new one because of the new environment and not enough knowledge about the culture and structure about the new organization they are going to work in. New employees in company normally need to integrate in their working place environment quicker to start doing their job. That makes them ask a lot of questions to their colleagues and sometimes their colleagues are too busy to answer those questions. In the literature is defined that this problem could be solved when new employees use digital system for information as the proposed system for searching of information. Furthermore, the quality of the returned results from the searching system is defined as a standard for the efficiency of the searching systems. Because of this, it is proposed a semantic system for searching information of employees in a company that will help to better orient new employees in a company, to know the position and the function of each employee in the company

Multi-agent Searching System for Medical Information

In the paper is proposed a model of multi-agent security system for searching a medical information in Internet. The advantages when using mobile agent are described, so that to perform searching in Internet. Nowadays, multi-agent systems found their application into distribution of decisions. For modeling the proposed multi-agent medical system is used JADE. Finally, the results when using mobile agent are generated that could reflect performance when working with BIG DATA. The proposed system is having also relatively high precision 96%.