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Scholarly knowledge graphs tackle source attribution with new provenance model

By PulseAugur Editorial · [1 sources] ·

This paper introduces a new approach to building scholarly knowledge graphs by addressing the complexities of source attribution and comparability. It proposes distinguishing between a working address for evidence retrieval, a source-defined category, and the decision to admit a passage for comparison. The authors demonstrate this method with the PaRAGraph Casebook on four re-runnable cases, showing how language models extract and verify claims. The system stores these decisions as objects, fixing the path from comparison to evidence while allowing the taxonomy to be revised with new sources. AI

IMPACT Introduces a framework for more robust and verifiable scholarly knowledge graphs, potentially improving AI-driven research synthesis.

RANK_REASON This is a research paper published on arXiv detailing a novel methodology for constructing scholarly knowledge graphs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

Scholarly knowledge graphs tackle source attribution with new provenance model

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This is a research paper published on arXiv detailing a novel methodology for constructing scholarly knowledge graphs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ksenia Kozha (American University of Cyprus), PaRAGraph Research Project ·

    Beyond source attribution: Address authorship and comparability decisions in an LLM-built scholarly knowledge graph

    arXiv:2610.28663v1 Announce Type: cross Abstract: A knowledge graph needs a stable address for its evidence even where the literature offers competing classifications and agrees on none. Yet an address chosen for retrieval readily passes for the only classification available. Thi…

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