One main challenge for search engines is retrieving the user’s intended results. Diversification techniques are employed to cover as many aspects of the query as possible through a tradeoff between the relevance of the results and the diversity in the result set. Most diversification techniques reorder the final result set. However, these diversification techniques could be inadequate for search scenarios with small candidate set sizes, or those for which response time is a critical issue. This paper presents a diversification technique for such scenarios. Instead of reordering the result set, the query is reformulated, thus taking advantage of the knowledge available in Linked Data Knowledge Bases. The query is annotated with semantic data and then expanded to related resources. An adapted Maximal Marginal Relevance technique is applied to select resources from this expanded set whose properties form the expanded query. Experiments conducted on federated and non-federated scenarios show that this method has superior diversification capacity and shorter response times than algorithms based on result set reordering.