Tags, as high quality semantic descriptors, are used in categorization, clustering and efficient retrieval of various items in the web corpus. Images, videos, songs and similar multimedia items are the most common items which are tagged either manually or in a semiautomatic manner. However, the tagging process becomes complicated when the content structure of an item is not interpretable. Such a problems occurs in items like scientific research datasets or documents with very little text content. In this work, we propose a generalized approach to automate tag expansion for such low-content items. We leverage intelligence of the web to generate secondary content for such items for the tag expansion process. While automating tag expansion, we also address the problem of topic drift by automating removal of the noisy tags from the set of candidate new tags. The effectiveness of the proposed approach is tested on a real world dataset. The performance of the proposed is compared with Wikipedia based nearest neighbor tagging (WikiSem) and non-negative matrix factorization (NMF) based tag expansion approaches. Based on the Mean Reciprocal Rank (MRR) metric, the proposed approach was twice as accurate as the WikiSem baseline (0.27 vs 0.13) and at least 2.25 times the NMF baselines (0.27 vs 0.12).