With the development of the Semantic Web, a lot of new structured data has become available on the Web in the form of knowledge bases (KBs). Making this valuable data accessible and usable for end-users is one of the main goals of question answering (QA) over KBs. Most current QA systems query oneKB, in one language (namely English). The existing approaches are not designed to be easily adaptable to new KBs and languages. We first introduce a new approach for translating natural language questions to SPARQL queries. It is able to query several KBs simultaneously, in different languages, and can easily be ported to other KBs and languages. In our evaluation, the impact of our approach is proven using 5 different well-known and large KBs: Wikidata, DBpedia, MusicBrainz, DBLP and Freebase as well as 5 different languages namely English, German, French, Italian and Spanish. Second, we show how we integrated our approach, to make it easily accessible by the research community and by end-users. To summarize, we provide a conceptional solution for multilingual, KB-agnostic question answering over the Semantic Web. The provided first approximation validates this concept.

中文翻译：

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走向语义网上的问答系统

随着语义Web的发展，许多新的结构化数据以知识库（KB）的形式在Web上可用。使这些有价值的数据可供最终用户访问和使用，是通过KB进行问题解答（QA）的主要目标之一。当前，大多数质量检查系统都以一种语言（即英语）查询oneKB。现有方法的设计目的并非易于适应新的知识库和语言。我们首先介绍一种将自然语言问题转换为SPARQL查询的新方法。它能够同时以不同的语言查询几个KB，并且可以轻松地移植到其他KB和语言。在我们的评估中，使用5种不同的知名大KB证明了我们方法的影响：Wikidata，DBpedia，MusicBrainz，DBLP和Freebase以及5种不同的语言，即英语，德语，法语，意大利语和西班牙语。其次，我们展示了如何整合我们的方法，以使研究社区和最终用户可以轻松地访问它。总而言之，我们为语义网上的多语言，与知识库无关的问题解答提供了一个概念性的解决方案。提供的第一近似值验证了此概念。