Question answering over knowledge graphs and other RDF data has been greatly
advanced, with a number of good systems providing crisp answers for natural
language questions or telegraphic queries. Some of these systems incorporate
textual sources as additional evidence for the answering process, but cannot
compute answers that are present in text alone. Conversely, systems from the IR
and NLP communities have addressed QA over text, but such systems barely
utilize semantic data and knowledge. This paper presents the first system for
complex questions that can seamlessly operate over a mixture of RDF datasets
and text corpora, or individual sources, in a unified framework. Our method,
called UNIQORN, builds a context graph on-the-fly, by retrieving
question-relevant evidences from the RDF data and/or a text corpus, using
fine-tuned BERT models. The resulting graph is typically rich but highly noisy.
UNIQORN copes with this input by a graph algorithm for Group Steiner Trees,
that identifies the best answer candidates in the context graph. Experimental
results on several benchmarks of complex questions with multiple entities and
relations, show that \uniqorn significantly outperforms state-of-the-art
methods for QA over heterogeneous sources. The graph-based methodology provides
user-interpretable evidence for the complete answering process.

UNIQORN 是一个基于图形方法的系统，它能够在混合的 RDF 数据和文本语料库以及单独的信息源中无缝地进行操作，该方法通过使用细化的 BERT 模型从 RDF 数据和文本语料库中检索与问题相关的证据来实时构建上下文图，然后使用基于图算法的 Group Steiner Trees 来确定上下文图中最佳答案候选项，实验结果表明其显著优于现有的异构数据源 QA 方法。