With the advance of natural language inference (NLI), a rising demand for NLI
is to handle scientific texts. Existing methods depend on pre-trained models
(PTM) which lack domain-specific knowledge. To tackle this drawback, we
introduce a scientific knowledge graph to generalize PTM to scientific domain.
However, existing knowledge graph construction approaches suffer from some
drawbacks, i.e., expensive labeled data, failure to apply in other domains,
long inference time and difficulty extending to large corpora. Therefore, we
propose an unsupervised knowledge graph construction method to build a
scientific knowledge graph (SKG) without any labeled data. Moreover, to
alleviate noise effect from SKG and complement knowledge in sentences better,
we propose an event-centric knowledge infusion method to integrate external
knowledge into each event that is a fine-grained semantic unit in sentences.
Experimental results show that our method achieves state-of-the-art performance
and the effectiveness and reliability of SKG.

通过引入科学知识图谱来使预训练模型适应科学领域并提出了无监督构建科学知识图谱的方法，进一步提出了以事件为中心的知识注入方法，实验结果表明，方法达到了最先进的性能并具有效果和可靠性。