Existing knowledge-based question answering systems often rely on small annotated
training data. While shallow methods like relation extraction are robust to data scarcity, they …

Templates are an important asset for question answering over knowledge graphs,
simplifying the semantic parsing of input utterances and generating structured queries for …

Question Answering (QA) systems aim at supplying precise answers to questions, posed by
users in a natural language form. They are used in a wide range of application areas, from …

Over the years, a growing number of semantic data repositories have been made available
on the web. However, this has created new challenges in exploiting these resources …

Conversational question answering (ConvQA) tackles sequential information needs where
contexts in follow-up questions are left implicit. Current ConvQA systems operate over …

Existing question answering methods infer answers either from a knowledge base or from
raw text. While knowledge base (KB) methods are good at answering compositional …

Question answering has been around for more than half a century. The problem was
addressed with different solutions in the eras of different technologies. Some proved more …

Question answering over knowledge bases (KB-QA) poses challenges in handling complex
questions that need to be decomposed into sub-questions. An important case, addressed …

知识图谱问答是通过处理用户提出的自然语言问题, 基于知识图谱的某种形式,
从中获取相关答案的过程. 由于知识规模, 计算能力及自然语言处理能力的制约 …

Question answering over RDF data like knowledge graphs has been greatly advanced, with
a number of good systems providing crisp answers for natural language questions or …