New benchmark and pipeline for schema induction from text released

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 →

• alphaXiv
• arXiv
• CatalyzeX
• Connected Papers
• DagsHub
• Gotit.pub
• Hugging Face
• Litmaps
• ScienceCast
• SCION
• scite Smart Citations
• SCOPE
• Text2Onto: A Framework for Ontology Learning and Data-Driven Change Discovery

• paper
• product

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