This document discusses using extended explicit semantic analysis (XESA) to measure semantic relatedness between short text snippets for recommendation purposes. It proposes enhancing ESA by incorporating additional semantic information from Wikipedia, such as article links and categories. An evaluation compares the performance of ESA, XESA using the article graph, XESA using categories, and a combination. The results show that XESA using the article graph improves over ESA by up to 9% and performs best for recommending related snippets.

1. Extended Explicit Semantic Analysis for Calculating Semantic Relatedness of Web Resources Presentation 2010/10/01 EC-TEL, Barcelona 2010-10-01 EC-TEL Presentation Scholl.ppt Recommendation WP WP WP WP WP WP

13. XESA – Overview ESA XESA AG XESA CAT XESA AG+CAT Article content Article Graph Category Information

21. Recommending via Semantic Relatedness Recommendation Semantic Relatedness (XESA) WP WP WP WP WP WP Paper excerpt: Social Network Analysis and Visualizations for Learning Web 2.0 Life long learning E-Learning TEL Blog entry: Visualization of Learning with Web 2.0

23. Questions? … Thank you for your attention! This work was supported by funds from the German Federal Ministry of Education and Research under the mark 01 PF 08015 A and from the European Social Fund of the European Union (ESF).