Scientific publishing has expanded significantly in recent decades, yet access to crucial research remains restricted for many, particularly in developing countries, independent researchers, and small academic institutions. The rising costs of journal subscriptions exacerbate this disparity, limiting the availability of knowledge even in well-funded universities. Despite the push for Open Access (OA), barriers persist, as demonstrated by large-scale access losses in Germany and the U.S. due to price disputes with publishers. This limitation hinders scientific progress, leading researchers to explore alternative methods for making scientific knowledge more accessible while navigating copyright constraints.Current methods of accessing scientific content primarily involve direct subscriptions, institutional access, or reliance on legally ambiguous repositories. These approaches are either financially unsustainable or legally contentious. While OA publishing helps, it does not fully resolve the accessibility crisis. Large Language Models (LLMs) offer a new avenue for extracting and summarizing knowledge from scholarly texts, but their use raises copyright concerns. The challenge lies in separating factual content from the creative expressions protected under copyright law.To address this, the research team proposes Project Alexandria, which introduces Knowledge Units (KUs) as a structured format for extracting factual information while omitting stylistic elements. KUs encode key scientific insightssuch as definitions, relationships, and methodological detailsin a structured database, ensuring that only non-copyrightable factual content is preserved. This framework aligns with legal principles like the idea-expression dichotomy, which states that facts cannot be copyrighted, only their specific phrasing and presentation.Reference: https://arxiv.org/pdf/2502.19413Knowledge Units are generated through an LLM pipeline that processes scholarly texts in paragraph-sized segments, extracting core concepts and their relationships. Each KU contains:Entities: Core scientific concepts identified in the text.Relationships: Connections between entities, including causal or definitional links.Attributes: Specific details related to entities.Context summary: A brief summary ensuring coherence across multiple KUs.Sentence MinHash: A fingerprint to track the source text without storing the original phrasing.This structured approach balances knowledge retention with legal defensibility. Paragraph-level segmentation ensures optimal granularitytoo small, and information is scattered; too large, and LLM performance degrades.From a legal standpoint, the framework complies with both German and U.S. copyright laws. German law explicitly excludes facts from copyright protection and allows data mining under specific exemptions. Similarly, the U.S. Fair Use doctrine permits transformative uses like text and data mining, provided they do not harm the market value of the original work. The research team demonstrates that KUs satisfy these legal conditions by excluding expressive elements while preserving factual content.To evaluate the effectiveness of KUs, the team conducted multiple-choice question (MCQ) tests using abstracts and full-text articles from biology, physics, mathematics, and computer science. The results show that LLMs using KUs achieve nearly the same accuracy as those given the original texts. This suggests that the vast majority of relevant information is retained despite the removal of expressive elements. Furthermore, plagiarism detection tools confirm minimal overlap between KUs and the original texts, reinforcing the methods legal viability.Beyond legal considerations, the research explores the limitations of existing alternatives. Text embeddings, commonly used for knowledge representation, fail to capture precise factual details, making them unsuitable for scientific knowledge extraction. Direct paraphrasing methods risk maintaining too much similarity to the original text, potentially violating copyright laws. In contrast, KUs provide a more structured and legally sound approach.The study also addresses common criticisms. While some argue that citation dilution could result from extracting knowledge into databases, traceable attribution systems can mitigate this concern. Others worry that nuances in scientific research may be lost, but the team highlights that most complex elementslike mathematical proofsare not copyrightable to begin with. Concerns about potential legal risks and hallucination propagation are acknowledged, with recommendations for hybrid human-AI validation systems to enhance reliability.The broader impact of freely accessible scientific knowledge extends across multiple sectors. Researchers can collaborate more effectively across disciplines, healthcare professionals can access critical medical research more efficiently, and educators can develop high-quality curricula without cost barriers. Additionally, open scientific knowledge promotes public trust and transparency, reducing misinformation and enabling informed decision-making.Moving forward, the team identifies several research directions, including refining factual accuracy through cross-referencing, developing educational applications for KU-based knowledge dissemination, and establishing interoperability standards for knowledge graphs. They also propose integrating KUs into a broader semantic web for scientific discovery, leveraging AI to automate and validate extracted knowledge at scale.In summary, Project Alexandria presents a promising framework for making scientific knowledge more accessible while respecting copyright constraints. By systematically extracting factual content from scholarly texts and structuring it into Knowledge Units, this approach provides a legally viable and technically effective solution to the accessibility crisis in scientific publishing. Extensive testing demonstrates its potential for preserving critical information without violating copyright laws, positioning it as a significant step toward democratizing access to knowledge in the scientific community.Check outthe Paper and Project.All credit for this research goes to the researchers of this project. Also,feel free to follow us onTwitterand dont forget to join our80k+ ML SubReddit. Recommended Read- LG AI Research Releases NEXUS: An Advanced System Integrating Agent AI System and Data Compliance Standards to Address Legal Concerns in AI DatasetsThe post Project Alexandria: Democratizing Scientific Knowledge Through Structured Fact Extraction with LLMs appeared first on MarkTechPost.