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The AI2 Reasoning Challenge (ARC), a new benchmark dataset for questionanswering (QA) has been recently released. ARC only contains natural sciencequestions authored for human exams, which are hard to answer and requireadvanced logic reasoning. On the ARC Challenge Set, existing state-of-the-artQA systems fail to significantly outperform random baseline, reflecting thedifficult nature of this task. In this paper, we propose a novel framework foranswering science exam questions, which mimics human solving process in anopen-book exam. To address the reasoning challenge, we construct contextualknowledge graphs respectively for the question itself and supporting sentences.Our model learns to reason with neural embeddings of both knowledge graphs.Experiments on the ARC Challenge Set show that our model outperforms theprevious state-of-the-art QA systems.

KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings