The decentral architecture behind the Web has led to pieces of in-
formation being distributed across data sources with varying structure. Hence,
answering complex questions often requires combining information from struc-
tured and unstructured data sources. We present an extension for HAWK, a novel
search approach for Hybrid Question Answering based on combining Linked
Data and textual data. Especially, we introduce our preliminary approach towards
answering ASK queries. This approach relies a classification heuristic next to
the existing predicate-argument representations of questions to derive equivalent
combinations of SPARQL query fragments and text queries. These are executed
so as to integrate the results of the text queries into SPARQL and thus generate a
formal interpretation of the query. Our results show that these developments lead
to HAWK achieving 0.74 F-measure on the ASK queries contained in the Ques-
tion Answering over Linked Data (QALD-5) hybrid query benchmark assuming
an given optimal ranking function.