Relevance feedback is one method for creating a ‘virtuous cycle’ - as put by Baeza-Yates - between semantics and search. Previous approaches to search have generally considered the Semantic Web and hypertext Web search to be entirely disparate, indexing and searching over different domains. While relevance feedback have traditionally improved information retrieval performance, relevance feedback is normally used to improve rankings of a single data-set. Our novel approach is to use relevance feedback from hypertext Web search to improve the retrieval of Semantic Web data. We also inspect whether relevance feedback from Semantic Web data can improve hypertext Web search results. In both cases, an evaluation based on certain kinds of informational queries (abstract concepts, people, and places) selected from a query log and human judges show that relevance feedback works: relevance feedback from hypertext Web search can improve the retrieval of Semantic Web data, and vice versa. We evaluate our work over a wide range of algorithms, and show it improves baseline performance on these queries for deployed systems as well, such as the semantic Search engine FALCON-S and the commercial Web search engine Yahoo! search.