The overview of the literature made within the scope of the current work proved the personalization to be an important issue for different types of software systems including Conceptual Content Management Systems. One of the problems of implementation of Personalization is the need to link the public and the private view to the system. The general approach to solve this problem is to provide some piece of software,responsible for linking these two views, i.e., mediating between them. During the current work the attempt to solve the problem of mediation between public and private modules of Conceptual Content Management Systems was made. The analysis of the problem showed that it is not possible to do this generically. Therefore, the Generator for the generation of Mediation Modules was designed and implemented. The Generator and a set of generic classes solve the mediation task for the Conceptual Content Management Systems. They are realize the full functionality needed to facilitate personalization of public assets, though some bugs can still be found. The Mediation Modules generated by the Generator developed were tested and proved to work on a simple asset definition model, containing two asset classes, one of which inherits from the other. The developed software still needs to be tested on more complex models. The value of this project consist in the first implementation of the powerful Personalization mechanism ensuring openness and dynamics of Conceptual Content Management Systems.

Abstract: Traditional content-based filtering methods usually utilize text extraction and classification techniques for building user profiles as well as for representations of contents, i.e. item profiles. These methods have some disadvantages e.g. mismatch between user profile terms and item profile terms, leading to low performance. Some of the disadvantages can be overcome by incorporating a common ontology which enables representing both the users ' and the items ' profiles with concepts taken from the same vocabulary. We propose a new content-based method for filtering and ranking the relevancy of items for users, which utilizes a hierarchical ontology. The method measures the similarity of the user's profile to the items ' profiles, considering the existing of mutual concepts in the two profiles, as well as the existence of "related " concepts, according to their position in the ontology. The proposed filtering algorithm computes the similarity between the users ' profiles and the items ' profiles, and rank-orders the relevant items according to their relevancy to each user. The method is being implemented in ePaper, a personalized electronic newspaper project, utilizing a hierarchical ontology designed specifically for classification of News items. It can, however, be utilized in other domains and extended to other ontologies.

Generally the searcher either searches for exact information based on the query or just surf topics which interest them on websites. Naturally, when user enters a query related to some topics, they did not get exact result of what they want. If the system selected the relevant passages, grouping together, made it summarizing and fluently, and returned the resulting text it will be an advantage to the user. Otherwise, if the resulting summary is not relevant enough to searcher, the user can refine the query. Thus, as a result, summarization is used as a technique for improving querying. To ensure this technique they proposed to summarize the content of a temporal topic in existing work by using an anatomy based summarization method called Topic Summarization and Content Anatomy (TSCAN). A temporal similarity (TS) function is implemented to generate the event dependencies and context similarity to form an evolution graph of the topic search. In this paper, we are combining two methods for topic summarization. The first method is mainly based on termfrequency, while the second method is based on ontology. We will construct an ontology database for analyzing the main topics of the article using NPL tool. understanding of a number of disparate fields, including linguistics, computer science, and statistics.