Industry Chain Graph Building Based on Text Semantic Association Mining

The current volume of data in the field of securities investment is increasing dramatically. Simultaneously, the linkage of data from multiple parties makes investment reasoning decisions more challenging than ever. In response to this problem, the financial field's knowledge graph can improve the efficiency, depth, and breadth of financial practitioners' information analysis. Some existing financial knowledge graphs analyze the shareholding relationship between companies. Still, because they are limited to observing data from the company's perspective, users without professional industry background cannot quickly find the industry factors of stock market changes. This paper proposes a financial knowledge graph from the industry chain's perspective. This paper builds upstream and downstream relationships between industries through Transformer-based bidirectional encoder to mine potential industry chain associations from text data and completes the long industry chain of the stock market. This paper also builds a visualization system to display and explore the connection between listed companies and industries. Users can inspect the industry chain's composition and each company's revenue status and stock market conditions in the industry chain. The experiment shows that when the market price fluctuation is detected, the stock price fluctuation can be traced back to its origin in the knowledge graph.