Complex query augmentation for question answering over knowledge graphs

Question answering systems have often a pipeline architecture that consists of multiple components. A key component in the pipeline is the query generator, which aims to generate a formal query that corresponds to the input natural language question. Even if the linked entities and relations to an underlying knowledge graph are given, finding the corresponding query that captures the true intention of the input question still remains a challenging task, due to the complexity of sentence structure or the features that need to be extracted. In this work, we focus on the query generation component and introduce techniques to support a wider range of questions that are currently less represented in the community of question answering.