KGCQual: An Interpretable Framework for Evaluating the Knowledge Graph Construction Quality from Text

Knowledge Graphs (KGs) are increasingly constructed through automated extraction pipelines; however, such systems often introduce spurious or incomplete triples, which degrade downstream performance. Existing evaluation practices rely heavily on task-specific metrics or small-scale manual verification, offering limited insight into the structural and semantic fidelity of extracted graphs. We propose a novel, interpretable metric for intrinsic KG quality assessment that measures how closely an au

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ethics.ai (11 July 2026), “KGCQual: An Interpretable Framework for Evaluating the Knowledge Graph Construction Quality from Text,” evidence record 47, https://ethics.ai/record/47 (originally published by arXiv).

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