Managing the semantic quality of the categorization in large textual
datasets, such as Wikipedia, presents significant challenges in terms of
complexity and cost. In this paper, we propose leveraging transformer models to
distill semantic information from texts in the Wikipedia dataset and its
associated categories into a latent space. We then explore different approaches
based on these encodings to assess and enhance the semantic identity of the
categories. Our graphical approach is powered by Convex Hull, while we utilize
Hierarchical Navigable Small Worlds (HNSWs) for the hierarchical approach. As a
solution to the information loss caused by the dimensionality reduction, we
modulate the following mathematical solution: an exponential decay function
driven by the Euclidean distances between the high-dimensional encodings of the
textual categories. This function represents a filter built around a contextual
category and retrieves items with a certain Reconsideration Probability (RP).
Retrieving high-RP items serves as a tool for database administrators to
improve data groupings by providing recommendations and identifying outliers
within a contextual framework.

使用转换器模型从维基百科数据集及其相关类别中提取语义信息，通过不同方法对类别的语义特征进行评估和增强，以提高数据组织的语义准确性。