Binocular attention-based stacked BiLSTM NER model for Supply chain management event knowledge graph construction

Extracting fine-grained event ontology knowledge based on supply chain management (SCM) related corpus and constructing knowledge graph (KG) has important guiding significance and knowledge support for the efficient implementation and development of SCM in manufacturing enterprises. Recently, research on the KG of SCM has not gained sufficient attention. This paper aims to propose an event logical KG construction approach for SCM. Specifically, a stacked BiLSTM entity recognition model based on the binocular attention mechanism is proposed, called the SBBAN model. Firstly, the character feature attention mechanism is used to infer the key information that contributes greatly to entity recognition in the text sequence. Character weighted features and character features splicing are used as new character input features. Then the deep semantic abstract features of text sequence are obtained by stacked BiLSTM. In addition, a self-attention mechanism is added to obtain the deep context relevant features. Experimental results show that the model shows better performance in in comparison with the state-of-the-art algorithms to complete the matching of event argument entities and offer knowledge support for SCM.