M.W. van Someren

Web Mining: From Web to Semantic Web

In this paper we look at the deployment of web usage mining results within two key application areas of web measurement and knowledge generation for personalisation. We take a fresh look at the model of interaction between business and visitors to their web sites and the sources of data generated during these interactions. We then look at previous attempts at measuring the effectiveness of the web as a channel to customers and describe our approach, based on scenario development and measurement to gain insights into customer behaviour. We then present Concerto, a platform for deploying knowledge on customer behaviour with the aim of providing a more personalized service. We also look at approaches to measuring the effectiveness of the personalization. Various standards that are emerging in the market that can ease the integration effort of personalization and similar knowledge deployment engines within the existing IT infrastructure of an organization are also presented. Finally, current challenges in the deployment of web usage mining are presented.

Semantics, Web and Mining

EWMF Papers.- A Website Mining Model Centered on User Queries.- WordNet-Based Word Sense Disambiguation for Learning User Profiles.- Visibility Analysis on the Web Using Co-visibilities and Semantic Networks.- Link-Local Features for Hypertext Classification.- Information Retrieval in Trust-Enhanced Document Networks.- Semi-automatic Creation and Maintenance of Web Resources with webTopic.- KDO Papers on KDD for Ontology.- Discovering a Term Taxonomy from Term Similarities Using Principal Component Analysis.- Semi-automatic Construction of Topic Ontologies.- Evaluation of Ontology Enhancement Tools.- KDO Papers on Ontology for KDD.- Introducing Semantics in Web Personalization: The Role of Ontologies.- Ontology-Enhanced Association Mining.- Ontology-Based Rummaging Mechanisms for the Interpretation of Web Usage Patterns.

Discovering stages in web navigation

Users of web sites often do not know exactly what they are looking for or what the site has to offer. During navigation they use the information found so far to formulate their information needs and refine their search. In these cases users need to pass through a series of pages before they can use the information that will eventually answer their question. Recommender systems aimed at leading users to target pages directly do not provide optimal assistance to these users. In this paper we propose a method to automatically divide web navigation into a number of stages. A recommender can use these stages to recommend pages which do not only match the topic of a users search, but also the current stage of the navigation process. As these recommendations are more tailored toward the users current situation, they can provide better assistance than recommendations made by traditional recommender systems.