While there has been substantial progress in factoid question-answering (QA),
answering complex questions remains challenging, typically requiring both a
large body of knowledge and inference techniques. Open Information Extraction
(Open IE) provides a way to generate semi-structured knowledge for QA, but to
date such knowledge has only been used to answer simple questions with
retrieval-based methods. We overcome this limitation by presenting a method for
reasoning with Open IE knowledge, allowing more complex questions to be
handled. Using a recently proposed support graph optimization framework for QA,
we develop a new inference model for Open IE, in particular one that can work
effectively with multiple short facts, noise, and the relational structure of
tuples. Our model significantly outperforms a state-of-the-art structured
solver on complex questions of varying difficulty, while also removing the
reliance on manually curated knowledge.

本研究提出了一种 Open IE 推理方法，利用最近提出的支持图优化框架进行 QA，从而使得可以更有效地处理所提出的方法中所述的多种难度级别的复杂问题。同时本方法不依赖于手动策划的知识。