Knowledge-Graph Relation Extraction for Chinese Business Datasets

This article presents the construction of a large-scale, high-quality Chinese Business Dataset (CBD) for knowledge graph construction in the Chinese business field. We consider the characteristics of the Chinese language and verify the effectiveness and reliability of the dataset through a series of experiments. The article discusses the process of extracting issues, entity links, and manual evaluation of the dataset. The authors conducted experiments using different relationship extraction algorithms based on the published dataset for training. The results suggest that Context-Aware is highly effective for relation classification in this task, while Bi-LSTM Attention may be insufficient. The use of knowledge graphs has enabled the organization of large amounts of structured and unstructured data, providing a more comprehensive understanding of business entities and their relationships. However, there are still challenges that need to be addressed in this field. Nevertheless, with the continuous advancement of technology and the increasing availability of data, further progress can be expected in this field.