Traditional information retrieval (IR) systems use keywords to index and retrieve documents. The limitations of keywords were recognized since the early days, specially when different but closely related words are used in the query and the relevant document. Query expansion techniques like pseudo-relevance feedback (PRF) and document clustering techniques rely on the target document set in order to bridge the gap between those words. This paper explores the use of knowledge-based semantic relatedness techniques to overcome the vocabulary mismatch between the query and documents, both on IR and Passage Retrieval for question answering. We performed query expansion and document expansion using WordNet, with positive effects over a language modeling baseline on three datasets, and over PRF on two of those datasets. Our analysis shows that our models and PRF are complementary; in that, PRF is better for easy queries, and our models are stronger for difficult queries and that our models generalize better to other collections, being more robust to parameter adjustments. In addition, we show that our method has a positive impact in an end-to-end question answering system for Basque and that it can be readily applied to other knowledge bases, as our good results using Wikipedia show, paving the way for the use of other knowledge structures such as medical ontologies and linked data repositories.