Manufacturing industry is the foundation of a country’s economic development and prosperity. At present, the data in manufacturing enterprises have the problems of weak correlation and high redundancy, which can be solved effectively by knowledge graph. In this paper, a method of knowledge graph construction in manufacturing domain based on knowledge enhanced word embedding model is proposed. The main contributions are as follows: (1) At the algorithmic level, this paper proposes KEWE-BERT, an end-to-end model for joint entity and relation extraction, which superimposes the token embedding and knowledge embedding output by BERT and TransR so as to improve the effect of knowledge extraction; (2) At the application level, knowledge representation model ManuOnto and dataset ManuDT are constructed based on real manufacturing scenarios, and KEWE-BERT is used to construct knowledge graph from them. The knowledge graph constructed has rich semantic relations, which can be applied in actual production environment. Other than that, KEWE-BERT can extract effective knowledge and patterns from redundant texts in the enterprise, which providing a solution for enterprise data management.
An Adversarial Transfer Network for Knowledge Representation Learning
