[Objective] This paper constructs knowledge graph based on the public resume data with natural language processing technology, which provides new tool for traditional data analysis. [Context] The proposed method could automatically extract profesional backgrounds and job information from resumes, and then obtain the relationship of working experience and colleagues in the organizations. The visualized knowledge graph could provide decision support for talent selection, personnel appointment and removal tasks of enterprises and institutions. [Methods] First, we used crawler to obtain the resume data and used the BERT-BiLSTM-CRF model to recognize entities. Then, we established the relationship between entities by defining rules and integrating the external domain knowledge. Finally, we used neo4j graph database to store and visualize data. [Results] The accuracy of the BERT-BiLSTM-CRF model with the entity recognition task was 84.85%. The constructed knowledge graph, which included resumes of 561 people, 8,174 entities in 3 categories, and 20,162 relationships in 5 categories, could support multi-angle queries and data mining. [Conclusions] This proposed model explores the internal relationships among resumes and provides a novel way to analyze resumes. However, there are few precise entity alignment processing and the establishment of relationships among institution entities.
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