发表机构
New Jersey Institute of Technology（新泽西理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出自主科学知识生成框架，整合多种技术将科学出版物转化为适用于人工智能的知识库，以电光材料为例进行验证，实现从文献到知识库的转变，为人工智能驱动的科学发现提供了可扩展、领域独立的基础。
AI 中文摘要
人工智能正在改变科学发现，但受结构化科学知识可用性的限制。现有数据库虽加速了数据驱动的材料研究，但预测建模和逆向设计所需知识仍存在于非结构化文献中。本文提出自主科学知识生成框架，将科学出版物转化为统一的、适用于人工智能的科学知识库。该框架整合了本体引导的文献获取、混合科学知识提取、语义协调、知识融合及验证。以电光材料为例进行概念验证，展示了从科学文献到适用于人工智能的知识库的完整转变，为预测性人工智能等提供了可扩展、领域独立的基础。
英文摘要
Artificial intelligence (AI) is transforming scientific discovery, but its effectiveness is fundamentally limited by the availability of structured scientific knowledge. Although existing databases have accelerated data-driven materials research, much of the knowledge needed for predictive modeling and inverse design remains embedded in unstructured scientific literature. We present an Autonomous Scientific Knowledge Generation Framework that transforms scientific publications into a Unified AI-Ready Scientific Knowledge Base. The framework integrates ontology-guided literature acquisition, hybrid scientific knowledge extraction, semantic harmonization, knowledge fusion, and validation within a unified workflow. Rather than treating literature retrieval, information extraction, and database construction as separate tasks, the framework progressively converts scientific publications into structured, semantically consistent, and provenance-preserving knowledge suitable for AI-driven reasoning. As a proof of concept, the framework was applied to electro-optic materials. Autonomous literature acquisition retrieved and validated about 1,000 publications from multiple scholarly repositories. A representative subset of eight publications was processed through the complete workflow, generating 29 structured scientific records that were harmonized into 7 canonical scientific records. The results demonstrate the complete transformation from scientific literature to an AI-ready scientific knowledge base while preserving quantitative measurements, operating conditions, provenance, and scientific context. The proposed framework provides a scalable, domain-independent foundation for predictive AI, generative AI, and closed-loop AI-driven scientific discovery.
Comments32 pages, 6 figures
