A link prediction method for Chinese financial event knowledge graph based on graph attention networks and convolutional neural networks

Finance is a knowledge-intensive domain in nature, with its data containing a significant amount of interconnected information. Constructing a financial knowledge graph is an important application for transforming financial text/web content into machine-readable data. However, the complexity of Chinese financial knowledge and the dynamic and evolving nature of Chinese financial data often lead to incomplete knowledge graphs. To address this challenge, we propose a novel link prediction method for Chinese financial event knowledge graph based on Graph Attention Networks and Convolutional Neural Networks. Our method begins with the construction of the foundational Chinese financial event knowledge graph using a relational triple extraction module integrated with a large language model framework, along with a Prompting with Iterative Verification (PiVe) module for validation. To enhance the completeness of the knowledge graph, we introduce an encoder-decoder framework, where a graph attention network with joint embeddings of financial event entities and relations acts as the encoder, while a Convolutional Knowledge Base embedding model (ConvKB) serves as the decoder. This framework effectively aggregates crucial neighbor information and captures global relationships among entity and relation embeddings. Extensive comparative experiments demonstrate the utility and accuracy of this method, ultimately enabling the effective completion of Chinese financial event knowledge graphs.