Ranked by the Matcher: A Reproducibility Audit of Knowledge Graph Extraction from Threat Reports
Security teams and researchers choose knowledge-graph extraction tooling for threat reports on the strength of published triple-F1 scores, yet those scores depend on how predicted triples are matched to gold annotations. We could reimplement the stated matching rule for only five of twelve inspected systems. Re-scoring ten system outputs on shared documents under eight protocols reverses eleven of forty-five pairwise orderings; one fixed prediction set spans 0.16--0.70 F1. On an external, human-adjudicated set, no mechanical matcher---lexical, embedding, or entailment---agrees with the reviewers more than seven times in ten; an LLM judge agrees far more often. To separate component effect...
| Authors | Safayat Bin Hakim, Houbing Herbert Song |
|---|---|
| Primary category | cs.CR |
| Published | 1 September 2026 |
| Last updated at source | 5 September 2026 |
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