Internal link optimization is a recurring task in search engine optimization, yet many production workflows rely on manual judgment, fixed page templates, or generic tool recommendations. Practitioners need ways to evaluate candidate links before deployment because link changes can redistribute authority and affect semantic coherence in ways that are difficult to isolate after release. We present WebKnoGraph, an open-source framework for evaluating internal linking strategies on website crawls. The framework models a website as a directed graph, represents pages by embeddings, scores candidate links with GraphSAGE, and evaluates interventions by embedding the site into larger host environments. We instantiate WebKnoGraph on a production crawl of Kalicube.com and compare automatic with expert-assisted link selection in an empirical FineWeb-based host graph and a synthetic Barabási-Albert host graph, using PageRank-based authority metrics and semantic coherence. The results show that automatic selection generally produces stronger authority redistribution, with higher Authority Yield, but also larger semantic coherence costs. Expert-assisted selection better preserves semantic coherence and, when targeting low-PageRank pages, achieves the highest Authority Yield, although with the least favorable loss-gain balance. Authority Volatility provides an additional stability perspective, but is interpreted cautiously because the two regimes use different numbers of intervention sets. These findings support a practical workflow in which candidate intervention sets are generated at scale, evaluated jointly across authority gain, volatility, loss-gain balance, and semantic coherence, and then reviewed for editorial deployability before implementation.

翻译：内部链接优化是搜索引擎优化中的一项常规任务，然而许多生产流程仍依赖人工判断、固定页面模板或通用工具推荐。从业者需要在部署前评估候选链接，因为链接变更可能以难以事后隔离的方式重新分配权威性和影响语义连贯性。我们提出WebKnoGraph——一个基于网站爬取数据评估内部链接策略的开源框架。该框架将网站建模为有向图，通过嵌入向量表示页面，利用GraphSAGE对候选链接进行评分，并通过将网站嵌入更大宿主环境来评估干预效果。我们在Kalicube.com的生产爬取数据上实例化WebKnoGraph，分别在基于FineWeb的实证宿主图和基于Barabási-Albert的合成宿主图中，将自动链接选择与专家辅助链接选择进行比较，采用基于PageRank的权威性指标和语义连贯性指标进行评估。结果表明：自动选择通常能产生更强的权威性再分配（具有更高的权威产出），但同时也带来更大的语义连贯性成本；专家辅助选择能更好地保持语义连贯性，且在针对低PageRank页面时达到最高的权威产出，但损-益平衡性最差。权威波动性提供了额外的稳定性视角，但由于两种机制使用了不同数量的干预集，需谨慎解读。这些发现支持一个实用工作流程：大规模生成候选干预集，综合评估权威增益、波动性、损-益平衡及语义连贯性，并在实施前进行编辑部署审查。