  Protein-protein interaction (PPI) extraction from published scientific
literature provides additional support for precision medicine efforts. However,
many of the current PPI extraction methods need extensive feature engineering
and cannot make full use of the prior knowledge in knowledge bases (KB). KBs
contain huge amounts of structured information about entities and
relationships, therefore plays a pivotal role in PPI extraction. This paper
proposes a knowledge-aware attention network (KAN) to fuse prior knowledge
about protein-protein pairs and context information for PPI extraction. The
proposed model first adopts a diagonal-disabled multi-head attention mechanism
to encode context sequence along with knowledge representations learned from
KB. Then a novel multi-dimensional attention mechanism is used to select the
features that can best describe the encoded context. Experiment results on the
BioCreative VI PPI dataset show that the proposed approach could acquire
knowledge-aware dependencies between different words in a sequence and lead to
a new state-of-the-art performance.
