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 bio-medicine to tourism. Their underlying knowledge source can be structured data (e.g. RDF graphs and SQL databases), unstructured data in the form of plain text (e.g. textual excerpts from Wikipedia), or combinations of the above. In this paper we survey the recent work that has been done in the area of stateless QA systems with emphasis on methods that have been applied in RDF and Linked Data, documents, and mixtures of these. We identify the main challenges, we categorize the existing approaches according to various aspects, we review 21 recent systems, and 23 evaluation and training datasets that are most commonly used in the literature categorized according to the type of the domain, the underlying knowledge source, the provided tasks, and the associated evaluation metrics.

中文翻译：

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关于链接数据和文档的问答系统的调查

问答 (QA) 系统旨在为用户以自然语言形式提出的问题提供准确的答案。它们用于广泛的应用领域，从生物医学到旅游业。它们的基础知识源可以是结构化数据（例如 RDF 图和 SQL 数据库）、纯文本形式的非结构化数据（例如来自维基百科的文本摘录）或上述内容的组合。在本文中，我们调查了最近在无状态 QA 系统领域所做的工作，重点是已应用于 RDF 和关联数据、文档以及这些的混合的方法。我们确定了主要挑战，我们根据各个方面对现有方法进行分类，我们审查了 21 个最近的系统，