Collaborative knowledge systems evolve through messy, user-generated hierarchies. This study proposes a centrality-based pruning method that cleanses the Wikipedia category network by identifying structural redundancies and inconsistencies. By relying solely on endogenous information, it demonstrates how collective curation and algorithmic structure can be combined to improve the organization of open knowledge — work that later became the basis for a Wikipedia-based benchmark for graph neural networks, now widely used in research.