In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential. However, existing IE toolkits have several non-trivial problems, such as not supporting multi-tasks, not supporting automatic updates. In this work, we present CollabKG, a learnable human-machine-cooperative IE toolkit for KG and EKG construction. Specifically, for the multi-task issue, CollabKG unifies different IE subtasks, including named entity recognition (NER), entity-relation triple extraction (RE), and event extraction (EE), and supports both KG and EKG. Then, combining advanced prompting-based IE technology, the human-machine-cooperation mechanism with LLMs as the assistant machine is presented which can provide a lower cost as well as a higher performance. Lastly, owing to the two-way interaction between the human and machine, CollabKG with learning ability allows self-renewal. Besides, CollabKG has several appealing features (e.g., customization, training-free, propagation, etc.) that make the system powerful, easy-to-use, and high-productivity. We holistically compare our toolkit with other existing tools on these features. Human evaluation quantitatively illustrates that CollabKG significantly improves annotation quality, efficiency, and stability simultaneously.

翻译：为了构建或扩展以实体和事件为中心的知识图谱（KG与EKG），信息抽取（IE）标注工具包不可或缺。然而，现有的IE工具包存在若干不可忽视的问题，例如不支持多任务、不支持自动更新等。本文提出CollabKG——一种面向KG与EKG构建的可学习人机协同IE工具包。具体而言，针对多任务问题，CollabKG统一了不同的IE子任务，包括命名实体识别（NER）、实体关系三元组抽取（RE）和事件抽取（EE），并同时支持KG与EKG的构建。在此基础上，结合先进的基于提示词的IE技术，本文提出以大型语言模型（LLM）作为辅助机器的人机协同机制，该机制能够以更低的成本获得更高的性能。最后，得益于人机双向交互，具备学习能力的CollabKG可实现自我更新。此外，CollabKG还具备多项优势特性（如定制化、免训练、传播性等），使得该系统功能强大、易于使用且具有高生产率。我们将本工具包与现有其他工具在这些特性上进行了全面比较。人工评估定量表明，CollabKG能同时显著提升标注质量、效率与稳定性。