Knowledge graph construction for cross enterprise product supply relationship of intelligent instruments

The massive resources of intelligent instruments provide data support for cross enterprise regional network collaborative manufacturing. However, the discrete distribution of data, a large amount of redundancy, and weak correlation limit system knowledge learning. This paper proposes an automatic construction method of a knowledge graph for the supply and demand matching of products and the relationship between upstream and downstream enterprises. Firstly, Stanford CoreNLP tool is used to achieve enterprise entity recognition. Secondly, considering the potential relationship between boundary words and product keywords, an improved CRF model is proposed to achieve product entity recognition. Then, a method for extracting enterprise products supply relationships based on relationship indicator lexicon and dependency syntax analysis is proposed to obtain the upstream and downstream relationships of enterprise products. Finally, the Neo4j database is used to store the knowledge graph of cross enterprise product supply relationship of intelligent instruments, and it is published on a third-party service platform to promote efficient resource sharing and collaborative manufacturing of intelligent instruments across enterprises.