Project Alexandria: Towards Freeing Scientific Knowledge from Copyright
  Burdens via LLMs
Paywalls, licenses and copyright rules often restrict the broad dissemination
and reuse of scientific knowledge. We take the position that it is both legally
and technically feasible to extract the scientific knowledge in scholarly
texts. Current methods, like text embeddings, fail to reliably preserve factual
content, and simple paraphrasing may not be legally sound. We urge the
community to adopt a new idea: convert scholarly documents into Knowledge Units
using LLMs. These units use structured data capturing entities, attributes and
relationships without stylistic content. We provide evidence that Knowledge
Units: (1) form a legally defensible framework for sharing knowledge from
copyrighted research texts, based on legal analyses of German copyright law and
U.S. Fair Use doctrine, and (2) preserve most (~95%) factual knowledge from
original text, measured by MCQ performance on facts from the original
copyrighted text across four research domains. Freeing scientific knowledge
from copyright promises transformative benefits for scientific research and
education by allowing language models to reuse important facts from copyrighted
text. To support this, we share open-source tools for converting research
documents into Knowledge Units. Overall, our work posits the feasibility of
democratizing access to scientific knowledge while respecting copyright.
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Christoph Schuhmann
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Gollam Rabby
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Gollam Rabby
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tourist800
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Ameya Prabhu
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Ameya Prabhu
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Tawsif Ahmed
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tawsif
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sleeping4cat
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Andreas Hochlehnert
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Andreas Hochlehnert
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libeanim
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Huu Nguyen
claimed_verified
2025-03-02T20:18:16.174Z
5fc6879e1c5ee87b1164876d
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Huu Nguyen
user
huu-ontocord
67c02d6aa15ac71dcf1c7554
Nick Akinci Heidrich
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Ludwig Schmidt
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Robert Kaczmarczyk
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Sören Auer
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Jenia Jitsev
claimed_verified
2025-02-28T12:15:55.422Z
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Jenia Jitsev
user
JJitsev
67c02d6aa15ac71dcf1c7559
Matthias Bethge
Project Alexandria: Towards Freeing Scientific Knowledge from Copyright
  Burdens via LLMs
Paywalls, licenses and copyright rules often restrict the broad dissemination
and reuse of scientific knowledge. We take the position that it is both legally
and technically feasible to extract the scientific knowledge in scholarly
texts. Current methods, like text embeddings, fail to reliably preserve factual
content, and simple paraphrasing may not be legally sound. We urge the
community to adopt a new idea: convert scholarly documents into Knowledge Units
using LLMs. These units use structured data capturing entities, attributes and
relationships without stylistic content. We provide evidence that Knowledge
Units: (1) form a legally defensible framework for sharing knowledge from
copyrighted research texts, based on legal analyses of German copyright law and
U.S. Fair Use doctrine, and (2) preserve most (~95%) factual knowledge from
original text, measured by MCQ performance on facts from the original
copyrighted text across four research domains. Freeing scientific knowledge
from copyright promises transformative benefits for scientific research and
education by allowing language models to reuse important facts from copyrighted
text. To support this, we share open-source tools for converting research
documents into Knowledge Units. Overall, our work posits the feasibility of
democratizing access to scientific knowledge while respecting copyright.
Using language models to convert scholarly documents into structured Knowledge Units can legally and effectively preserve and disseminate scientific knowledge.