将材料科学综述转化为可机器查询的结构化知识，提升研究可重用性。
Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence
- 将综述表格转为可查询的结构化数据，融入开放科研知识图谱
- 符号查询在准确性上优于大模型查询，更适合作为可靠基础
- 适合需要精准、可验证知识的材料研发与智能系统开发者
科学综述在材料科学中起着知识整合的核心作用，但其关键信息仍被锁在叙述性文本和静态PDF表格中，限制了人类和机器的复用。本文以原子层沉积与刻蚀（ALD/E）为例，将综述表格发布为符合FAIR原则、可被机器操作的对比数据，存入开放科研知识图谱（ORKG），使其成为可查询的结构化知识。在此基础上，我们对比了基于符号查询与基于大语言模型的查询方式，主张在材料科学的神经符号人工智能中，应保持经人工梳理的符号层作为核心，而将大语言模型作为符号基础之上的补充接口，而非独立的真理来源。
原文摘要 · Abstract (English)
Scientific reviews are central to knowledge integration in materials science, yet their key insights remain locked in narrative text and static PDF tables, limiting reuse by humans and machines alike. This article presents a case study in atomic layer deposition and etching (ALD/E) where we publish review tables as FAIR, machine-actionable comparisons in the Open Research Knowledge Graph (ORKG), turning them into structured, queryable knowledge. Building on this, we contrast symbolic querying over ORKG with large language model-based querying, and argue that a curated symbolic layer should remain the backbone of reliable neurosymbolic AI in materials science, with LLMs serving as complementary, symbolically grounded interfaces rather than standalone sources of truth.
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