用加权自组织映射交互构建概念分类体系，兼顾效率与控制力。
TaBIIC2: Interactive Building of Ontological Taxonomies using Weighted Self-Organizing Maps
- 基于加权自组织映射自动发现数据中概念聚类。
- 支持用户逐步定义概念，实现对分类结果的可控干预。
- 适合需要精细构建领域知识体系的研究者使用。
本体表示领域的概念性知识。本体的核心是概念及其子概念的层级结构，其构建过程复杂。在许多情况下，相关信息以描述实体特征的表格数据形式存在。分析此类数据中的模式和相似性，可作为识别概念并组织它们的基础。然而，手动操作困难，纯自动化方法（如层次聚类或依赖大语言模型分析）往往产生过多结果且缺乏用户控制。本文提出一种工具，通过识别聚类及其意图性定义，支持渐进式、交互式构建概念分类体系。该工具采用加权自组织映射作为聚类方法，因其能根据特定实体特征值分布生成任意数量且互异的聚类。实验表明，结合快速创建概念的机制，该工具在完全手动与全自动方法之间提供了良好平衡。
原文摘要 · Abstract (English)
Ontologies represent the conceptual knowledge of a domain. At the core of an ontology is the taxonomy of concepts and subconcepts that represent specific entities, which can be complex to build. In many cases, information is available in the form of records describing the characteristics of relevant entities, i.e., tabular data. Identifying patterns and similarities in such data can serve as a basis for identifying concepts and organizing them. However, doing so manually can be challenging, and purely automatic approaches, such as agglomerative clustering or relying on a large language model to analyze the data, can leave the user with overwhelming results and little control. In this paper, we describe a tool that enables the progressive and interactive construction of a taxonomy of concepts by identifying clusters as well as their intentional definitions. To do so, we rely on weighted self-organizing maps as a clustering method because they enable the creation of an arbitrary number of clusters that are distinct with respect to the distributions of values of specific characteristics of the clustered entities. We show that, by integrating this mechanism and others for rapidly creating concepts that group together instances from tabular data, this tool represents a middle ground between purely manual analysis and automatic methods for building ontological taxonomies.
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