Ontology-driven personalized query refinement
Journal of Web Engineering
Masquerade attacks based on user's profile
Journal of Systems and Software
Generating SPARQL queries using templates
Web Intelligence and Agent Systems
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This thesis presents algorithms and techniques for increasing a search service's understanding of web queries. Existing search services rely solely on a query's occurrence in the document collection to locate relevant documents. They typically do not perform any task or topic-based analysis of queries using other available resources, and do not leverage changes in user query patterns over time. Provided within are a set of techniques and metrics for performing temporal analysis on query logs. The metrics proposed for our log analysis are shown to be reasonable and informative, and can be used to detect changing trends and patterns in the query stream, thus providing valuable data to a search service. We continue with an algorithm for automatic topical classification of web queries. Results are presented showing that our classification approach can be successfully applied to a significant portion of the query stream, making it possible for search services to leverage it for improving search effectiveness and efficiency.