  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.
