The emergence of Linked Data in the form of knowledge graphs in RDF has been one of the most recent evolutions of the Semantic Web. This led to the development of question answering systems based on RDF and SPARQL to allow end users to access and benefit from these knowledge graphs. However, a lot of information on the Web is still unstructured, which restricts the ability of answering questions whose answer does not exist in a knowledge base. To tackle this issue, hybrid question answering has emerged as an important challenge. In fact, hybrid question answering entails the task of question answering by combining both structured (RDF) and unstructured knowledge sources (text) into one answer. This thesis tackles hybrid question answering based on natural language questions. It focuses on the analysis and improvement of an open source system called HAWK, identifies its limitations and provides solutions and recommendations in the form of a generic question-answering pipeline called HAWK_R. Our system mostly uses heuristic methods, patterns and the ontological schema and knowledge base and provides three main additions: question classification, annotation and answer verification and ranking based on query content. Our results show a clear improvement over the original HAWK based on several Question Answering over Linked Data (QALD) competitions. In fact, our methods are not limited to HAWK and can also help increase the performance of other question answering systems.