Taxonomy building is a task that requires interpreting and classifying data within a given frame of reference, which comes to play in many areas of application that deal with knowledge and information organization. In this paper, we explore how taxonomy building can be supported with systems that integrate machine learning (ML). However, relying only on black-boxed ML-based systems to automate taxonomy building would sideline the users' expertise. We propose an approach that allows the user to iteratively take into account multiple model's outputs as part of their sensemaking process. We implemented our approach in two real-world use cases. The work is positioned in the context of HCI research that investigates the design of ML-based systems with an emphasis on enabling human-AI collaboration.

翻译：分类体系构建是一项需要在特定参照系内解释和分类数据的任务，广泛应用于知识与信息组织的多个领域。本文探讨如何通过集成机器学习（ML）的系统来支持分类体系构建。然而，仅依赖基于ML的黑盒系统来自动化分类构建将边缘化用户的专业判断。我们提出了一种方法，允许用户在意义建构过程中迭代地整合多个模型的输出结果。该方法已在两个实际应用案例中得到验证。本项研究属于人机交互（HCI）领域，侧重于通过设计基于ML的系统来赋能人机协作。