Enhancing Query Expansion through Folksonomies and Semantic Classes

Adaptive query expansion (QE) allows users to better define their search domain by supplementing the original query with additional terms related to their preferences and information needs. The system we present is an extension of the traditional QE techniques, which rely on the computation of two-dimensional co-occurrence matrices. Our system makes use of three-dimensional co-occurrence matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services such as delicious, Digg, and StumbleUpon. The results of an in-depth experimental evaluation on artificial datasets and real users show that our system outperforms some well-known approaches in the literature, as well as a state-of-the-art search engine.