{
  "id": 47,
  "url": "https://arxiv.org/abs/2607.10212v1",
  "title": "KGCQual: An Interpretable Framework for Evaluating the Knowledge Graph Construction Quality from Text",
  "summary": "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",
  "authors": "Nipun Misra, Vikranth Udandarao, Aanchal Gupta, Yogender Kumar, Manuj Mukherjee, Raghava Mutharaju",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-11T08:45:15.000Z",
  "fetched_at": "2026-07-14T14:14:15.665Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/47",
  "original_url": "https://arxiv.org/abs/2607.10212v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}