LEARNING SEMANTIC WEB USAGE PROFILES BY USING GENETIC ALGORITHMS

Web usage profile is very important in recommender systems. More interesting is the semantic enriched profile, which can describe visitor intents by ontologies and express more information and relations of visitor's character. Our research is based on processing semantically enriched clickstream and application of scoring algorithm, which is based on symbolic regression. A semantic enrichment, in other words the annotation of web pages, uses Linked Data principles. The scoring assigns to each pageview a value, which represents and involves visitor interests. Scoring involves all know attributes of each pageview including semantic annotation. The score of each pageview is used to establish a visitor profile. The established profile can be in form of ontologies. In this paper, we propose integrate scoring algorithm into semantic web usage mining and publish visitor profile in RDF/OWL representation. We suggest merge the profiles from different web sites and integrate additional related information from publicly available resources.