Term suggestion techniques recommend query terms to a user based on his initial query. Providing adequate term suggestions is a challenging task. Most existing commercial search engines suggest search terms based on the frequency of prior used terms that match the first few letters typed by the user. We present a novel mechanism to construct semantic term-relation graphs to suggest semantically relevant search terms. We build term relation graphs based on multi-partite networks of existing social media. These linkage networks are extracted from Wikipedia to eventually form term relation graphs. We propose incorporating contributor-category networks to model the contributor expertise. This step has been shown to significantly enhance the accuracy of the inferred relatedness of the term-semantic graphs. Experiments showed the obvious advantage of our algorithms over existing approaches © 2010 IEEE.