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New LLM pipeline Eolas extracts knowledge graphs from scientific texts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new pipeline called Eolas that leverages large language models to extract structured knowledge graphs from scientific texts, specifically focusing on irradiated materials. This system automates the process of transforming unstructured documents into knowledge graphs aligned with a specified ontology, significantly reducing the time required for data extraction compared to manual methods. The project also introduces a benchmark dataset for evaluating LLMs in this domain and provides practical guidelines for knowledge graph extraction. AI

IMPACT Automates scientific data extraction, potentially accelerating materials science research and discovery.

RANK_REASON The cluster contains an academic paper detailing a new method and dataset for LLM application in scientific knowledge extraction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New LLM pipeline Eolas extracts knowledge graphs from scientific texts

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The cluster contains an academic paper detailing a new method and dataset for LLM application in scientific knowledge extraction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Marco Luca Sbodio, Marcos Mart\'inez Galindo, Vanessa Lopez, Blanca Biel, Pablo Canca, Pedro Delgado, Jes\'us I. Mendieta-Moreno, Raphael Tack, Maria J. Caturla ·

    Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models

    arXiv:2609.17291v1 Announce Type: new Abstract: The quest for new materials increasingly relies on predictive models and comprehensive simulations that span scales from atomic to macroscopic levels. However, essential data necessary for these models and simulations are often embe…

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