  Current event-centric knowledge graphs highly rely on explicit connectives to
mine relations between events. Unfortunately, due to the sparsity of
connectives, these methods severely undermine the coverage of EventKGs. The
lack of high-quality labelled corpora further exacerbates that problem. In this
paper, we propose a knowledge projection paradigm for event relation
extraction: projecting discourse knowledge to narratives by exploiting the
commonalities between them. Specifically, we propose Multi-tier Knowledge
Projection Network (MKPNet), which can leverage multi-tier discourse knowledge
effectively for event relation extraction. In this way, the labelled data
requirement is significantly reduced, and implicit event relations can be
effectively extracted. Intrinsic experimental results show that MKPNet achieves
the new state-of-the-art performance, and extrinsic experimental results verify
the value of the extracted event relations.
