中文标题#
透過語義、譜和結構度量評估知識圖譜複雜性以進行鏈接預測
英文标题#
Evaluating Knowledge Graph Complexity via Semantic, Spectral, and Structural Metrics for Link Prediction
中文摘要#
理解數據集的複雜性對於在知識圖譜（KGs）上評估和比較鏈接預測模型是基礎性的。 雖然從譜聚類框架內類之間的概率差異中得出的累積譜梯度（CSG）指標被提出作為一種與分類器無關的複雜性指標，據稱其隨著類別基數的增長而變化，並與下游性能相關，但迄今為止尚未在 KG 環境中進行評估。 在本工作中，我們批判性地考察了在多關係鏈接預測背景下的 CSG，並通過 Transformer 派生的嵌入引入語義表示。 與之前的說法相反，我們發現 CSG 對參數化高度敏感，並不穩健地隨著類別數量增長。 此外，它與標準性能指標如平均倒數排名（MRR）和 Hit@1 的相關性較弱或不一致。 為了深入分析，我們引入並基準測試了一組結構和語義 KG 複雜性指標。 我們的研究結果表明，通過關係熵、節點級別的最大關係多樣性以及關係類型基數捕獲的全局和局部關係模糊性與 MRR 和 Hit@1 表現出強烈的反向相關性，表明這些指標作為任務難度的更忠實指示器。 相反，圖連通性度量如平均度、度熵、PageRank 和特徵向量中心性與 Hit@10 正相關。 我們的結果表明，CSG 所聲稱的穩定性和泛化預測能力在鏈接預測設置中未能成立，並強調了在知識驅動學習中需要更穩定、可解釋和任務對齊的數據集複雜性度量。
英文摘要#
Understanding dataset complexity is fundamental to evaluating and comparing link prediction models on knowledge graphs (KGs). While the Cumulative Spectral Gradient (CSG) metric, derived from probabilistic divergence between classes within a spectral clustering framework, has been proposed as a classifier agnostic complexity metric purportedly scaling with class cardinality and correlating with downstream performance, it has not been evaluated in KG settings so far. In this work, we critically examine CSG in the context of multi relational link prediction, incorporating semantic representations via transformer derived embeddings. Contrary to prior claims, we find that CSG is highly sensitive to parametrisation and does not robustly scale with the number of classes. Moreover, it exhibits weak or inconsistent correlation with standard performance metrics such as Mean Reciprocal Rank (MRR) and Hit@1. To deepen the analysis, we introduce and benchmark a set of structural and semantic KG complexity metrics. Our findings reveal that global and local relational ambiguity captured via Relation Entropy, node level Maximum Relation Diversity, and Relation Type Cardinality exhibit strong inverse correlations with MRR and Hit@1, suggesting these as more faithful indicators of task difficulty. Conversely, graph connectivity measures such as Average Degree, Degree Entropy, PageRank, and Eigenvector Centrality correlate positively with Hit@10. Our results demonstrate that CSGs purported stability and generalization predictive power fail to hold in link prediction settings and underscore the need for more stable, interpretable, and task-aligned measures of dataset complexity in knowledge driven learning.
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