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 multipartite networks of existing social media. These linkage networks are extracted from Wikipedia to eventually form term relation graphs. We propose incorporating contributorcategory 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.
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