The growing amount of data available in the Document Web as well as in the Linked Data Web has lead to an information gap. Information needed to answer complex questions might often require full-text data as well as Linked Data. Thus, HAWK combines unstructured and structured data sources. In this article, we introduce HAWK, a novel entity search approach for hybrid question answering based on combining Linked Data and textual data. In this article, we compare three ranking mechanism and evaluate their performance on the QALD-5 challenge. Finally, we identify the weak points of our current version of HAWK and give directions for future development.