Comparing Instances of Ontological Concepts for Personalized Recommendation in Large Information Spaces

We present a novel method for instance comparison of ontological concepts with regard to personalized content presentation and/or navigation in large information spaces. We assume that comparing properties of documents which users found interesting leads to discovery of information about users' interests specifically when considering Semantic Web applications where documents or their parts are represented by ontological concepts. We employ the ontology structure and different similarity metrics for datatype and object properties and investigate reasons behind user interest in the presented content. Moreover, we propose and evaluate an approach to instance similarity computation for a particular user while also considering the user's individual preferences

Improving Adaptive Hypermedia by Adding Semantics

Abstract. Recently the Web and its services have changed the way of thinking about them; hereby expectations have changed, too. While in recent times users have been satisfied with presentation of generic information, nowadays they are asking for personalized information, which are adapting to them. This kind of presentation is not sufficient in the information acquiring from databases on the Web. It is needed to append metadata to this information. Metadata express semantics and makes the information easier for computer processing. This paper presents models for adaptive and semantic Web and shows how to improve adaptive presentation with semantics

User Characteristics Acquisition from Logs with Semantics

Abstract. IVIany current web-based information systems need to adapt to individual user characteristics in order to streamline common tasks and enable users to use them efficiently. In this paper we describe an approach to user characteristics acquisition based on automatic analysis of user behavior thus minimizing the amount of necessary user involvement. We stress the importance of appropriate data collection and propose a novel approach to logging, in which we preserve the semantics of logged evelrts. This has a significant inrpact on the successive log analysis, which focuses on the estimation of user characteristics from user navigation