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New benchmark and pipeline for schema induction from text released

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SCOPE, a new benchmark designed to evaluate schema induction and fusion from raw text for information extraction and knowledge graph construction. Alongside SCOPE, they presented SCION, an auditable reference pipeline that uses candidate spaces and strict JSON contracts for schema construction and normalization. SCION-lite achieved the highest F1 score among comparable baselines on the SCOPE benchmark, with a variant, SCION-RL, reducing reliance on proprietary LLMs. AI

IMPACT This research could improve the efficiency and accuracy of knowledge graph construction and information extraction systems.

RANK_REASON The cluster describes a new academic paper introducing a benchmark and a reference pipeline for schema induction from text. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark and pipeline for schema induction from text released

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The cluster describes a new academic paper introducing a benchmark and a reference pipeline for schema induction from text. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Miaobo Hu, Xiaobo Guo, Shuhao Hu, Bokun Wang, Rui Chen, Xin Wang, Daren Zha, Jun Xiao ·

    SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text

    arXiv:2607.21610v1 Announce Type: cross Abstract: Schema graphs are an upstream bottleneck of schema-grounded information extraction and knowledge graph construction, yet most extraction systems assume the schema is already available. We introduce SCOPE (Schema Construction and O…

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