KnowACT : A Deep Semantic Multi‐task Knowledge Annotation Platform for Ancient Chinese Texts
Jie Jian, Jiayi Li, Chengxi Yan, Jianguang Hua · Proceedings of the Association for Information Science and Technology · 2025
ABSTRACT With the ongoing advancement of big data and AI, automatic extraction of knowledge units from ancient Chinese texts (ACTs) has become a key focus in Chinese natural language processing. However, existing solutions often suffer from limited task coverage, inadequate quality evaluation, and challenges posed by the unique linguistic features of ACTs. These factors collectively hinder the broader adoption of intelligent ACT processing systems. To address these issues, we proposed a multi‐task semantic annotation and generation system, named “KnowACT”, which includes a data loading layer, a task processing layer, and a result output layer. Our preliminary experiment has shown that when compared to other advanced annotation systems, KnowACT has significant advantages in the aspects of task functional integrity, annotation efficiency, and quality control of annotation texts. It is believed that KnowACT can promote the development of knowledge extraction technology and relevant systems for ACTs.