To keep an overview of a complex corporate web sites, it is crucial to understand the relationship of contents, structure and the user's behavior. In this paper, we describe an approach which is allowing us to compare web page content with the information implicitly defined by the structure of the web site. We start by describing each web page with a set of key words. We combine this information with the link structure in an algorithm generating a context based description. By comparing both descriptions, we draw conclusions about the semantic relationship of a web page and its neighborhood. In this way, we indicate whether a page fits in the content of its neighborhood. Doing this, we implicitly identify topics which span over several connected web pages. With our approach we support redesign processes by assessing the actual structure and content of a web site with designer's concepts.
Abstract. Our contribution in this paper is an approach to measure semantical relations within a web site. We start with a web page description by key words. The implementation of structural and content information reduces the variety of key words. Thereby, the documentkey-word-matrix is smoothend and similarities between web pages are emphasized. This increases the possibility of cluster key words and identif topics successfully. To do so, we implement a probabilistic clustering algorithm. To assess semantic relations, we introduce a number of measures and interpret them.
Abstract. The design and organization of a website reflects the authors intent. Since user perception and understanding of websites may differ from the authors, we propose a means to identify and quantify this difference in perception. In our approach we extract perceived semantic focus by analyzing user behavior in conjunction with keyword similarity. By combining usage and content data we identify user groups with regard to the subject of the pages they visited. Our real world data shows that these user groups are nicely distinguishable by their content focus. By introducing a distance measure of keyword coincidence between web pages and user groups, we can identify pages of similar perceived interest. A discrepancy between perceived distance and link distance in the web graph indicates an inconsistency in the web site's design. Determining usage similarity allows the web site author to optimize the content to the users needs.
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