Open Domain Question Answering (QA) is evolving from complex pipelined
systems to end-to-end deep neural networks. Specialized neural models have been
developed for extracting answers from either text alone or Knowledge Bases
(KBs) alone. In this paper we look at a more practical setting, namely QA over
the combination of a KB and entity-linked text, which is appropriate when an
incomplete KB is available with a large text corpus. Building on recent
advances in graph representation learning we propose a novel model, GRAFT-Net,
for extracting answers from a question-specific subgraph containing text and KB
entities and relations. We construct a suite of benchmark tasks for this
problem, varying the difficulty of questions, the amount of training data, and
KB completeness. We show that GRAFT-Net is competitive with the
state-of-the-art when tested using either KBs or text alone, and vastly
outperforms existing methods in the combined setting. Source code is available
at this https URL .

该论文提出了一种新模型 Graft-Net，应用于 Open Domain Question Answering 问题，该模型结合了文本和知识库并利用子图表示法以提高效率和准确度