A Dynamic Financial Knowledge Graph Based on Reinforcement Learning and Transfer Learning

The knowledge graph is a means of visualizing data to aid information analysis and understanding. In this paper, we construct a novel dynamic financial knowledge graph, which utilizes time information to capture data changes and trends over time. Firstly, the basic dynamic financial knowledge graph is constructed through structured and semi-structured data related to A-share. Then, using the transfer learning algorithms, we train the financial entity recognition models based on BERT, BiLSTM, and CRF. Next, we train the financial entity linking models based on similarity features and prior knowledge. After that, to alleviate the noise brought by distant supervision, we explore to train the financial relation classification models with the help of reinforcement learning. Finally, we implement the dynamic knowledge graph based on these models and their predictions. Additionally, a display website is designed and implemented to dynamically display the structural changes of the knowledge graph over time. The financial knowledge graph constructed in this paper is practical and the construction pipeline provides insights for a professional dynamic knowledge graph as well.