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Question Answering (QA) systems provide easy access to the vast amount ofknowledge without having to know the underlying complex structure of theknowledge. The research community has provided ad hoc solutions to the key QAtasks, including named entity recognition and disambiguation, relationextraction and query building. Furthermore, some have integrated and composedthese components to implement many tasks automatically and efficiently.However, in general, the existing solutions are limited to simple and shortquestions and still do not address complex questions composed of severalsub-questions. Exploiting the answer to complex questions is further challengedif it requires integrating knowledge from unstructured data sources, i.e.,textual corpus, as well as structured data sources, i.e., knowledge graphs. Inthis paper, an approach (HCqa) is introduced for dealing with complex questionsrequiring federating knowledge from a hybrid of heterogeneous data sources(structured and unstructured). We contribute in developing (i) a decompositionmechanism which extracts sub-questions from potentially long and complex inputquestions, (ii) a novel comprehensive schema, first of its kind, for extractingand annotating relations, and (iii) an approach for executing and aggregatingthe answers of sub-questions. The evaluation of HCqa showed a superior accuracyin the fundamental tasks, such as relation extraction, as well as thefederation task.

HCqa: Hybrid and Complex Question Answering on Textual Corpus and Knowledge Graph