Ontology-based partitioning of data steam for Web mining: A case study of Web logs

Abstract

This paper presents a nevel method partitioning steaming data based on ontology. Web directory service is applied to enrich semantics to web logs, as categorizing them to all possible hierarchical paths. In order to detect the candidate set of session identifiers, semantic factors like semantic mean, deviation, and distance matrix are established. Eventually, each semantic session is obtained based on nested repetition of top-down partitioning and evaluation process. For experiment, we applied this ontology-oriented heuristics to sessionize the access log files for one week from IRCache. Compared with time-oriented heuristics, more than 48% of sessions were additionally detected by semantic outlier analysis.