Research on the construction of event logic knowledge graph of supply chain management

Abstract

Knowledge graph technology plays an important role in knowledge supporting for efficient supply chain management (SCM) of manufacturing enterprises, a SCM knowledge graph can be constructed based on the relevant case corpus. In order to solve the problems of coarse concept granularity of event ontology knowledge in SCM cases, potential words of characters in the existing entity recognition models may not be matched or matched incorrectly, key features of entities with different lengths may interfere with each other in the expression of attention mechanism, and lack of a large number of annotation training samples, a top-down construction method of SCM event logic knowledge graph (ELKG) is proposed. Firstly, SCM event argument classes and class relations are defined, an event logic ontology model is built, and the event argument entities according to event logic ontology are labeled. Then, an active learning event argument entity recognition (EAER) model based on two-stage generative adversarial network (GAN) is proposed. In GAN generator, an EAER model based on binocular attention-based stacked BiLSTM with CNN (BACSBN) is proposed, which combines word-level character feature attention mechanism and n-gram pooling feature attention mechanism to improve the attention to character features of constituent words and highlight entity key information with different lengths, respectively. Two-stage GAN adversarial training and label space attention mechanism are introduced to select the correct predicted label samples for active learning training. The experimental results show that BACSBN can improve the entity recognition accuracy, and the two-stage GAN’s active learning can further improve the recognition effect on the basis of full annotation sample training, and can still maintain a high accuracy in the absence of a large number of manual annotation data. Further, according to the sentence pattern and keyword matching, the matching relations of argument entities are completed, and the SCM ELKG is constructed to provide knowledge support for autonomous SCM.