Improved algorithms for topic distillation in a hyperlinked environment
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Mining the Web: Discovering Knowledge from HyperText Data
Mining the Web: Discovering Knowledge from HyperText Data
Web unit mining: finding and classifying subgraphs of web pages
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Real-time data pre-processing technique for efficient feature extraction in large scale datasets
Proceedings of the 17th ACM conference on Information and knowledge management
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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 implictly 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 neighbourhood. In this way, we indicate whether a page fits in the content of its neighbourhood. 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.