Journal of East China Normal University(Natural Science) ›› 2021, Vol. 2021 ›› Issue (5): 24-36.doi: 10.3969/j.issn.1000-5641.2021.05.003

• Financial Knowledge Graph • Previous Articles     Next Articles

Joint extraction of entities and relations for domain knowledge graph

Rui FU, Jianyu LI, Jiahui WANG, Kun YUE*(), Kuang HU   

  1. School of Information Science and Engineering, Yunnan University, Kunming 650500, China
  • Received:2021-08-05 Online:2021-09-25 Published:2021-09-28
  • Contact: Kun YUE


Extraction of entities and relationships from text data is used to construct and update domain knowledge graphs. In this paper, we propose a method to jointly extract entities and relations by incorporating the concept of active learning; the proposed method addresses problems related to the overlap of vertical domain data and the lack of labeled samples in financial technology domain text data using the traditional approach. First, we select informative samples incrementally as training data sets. Next, we transform the exercise of joint extraction of entities and relations into a sequence labeling problem by labelling the main entities. Finally, we fulfill the joint extraction using the improved BERT-BiGRU-CRF model for construction of a knowledge graph, and thus facilitate financial analysis, investment, and transaction operations based on domain knowledge, thereby reducing investment risks. Experimental results with finance text data shows the effectiveness of our proposed method and verifies that the method can be successfully used to construct financial knowledge graphs.

Key words: domain text, domain knowledge graph, joint extration of entities and relations, active learning, sequence labeling

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