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.

翻译：科学综述在材料科学知识整合中具有核心地位，但其关键见解仍被封存于叙述性文本和静态PDF表格中，限制了人类与机器的重复利用。本文以原子层沉积与刻蚀（ALD/E）为案例研究，将综述表格以FAIR（可查找、可访问、可互操作、可重用）的机器可操作比较形式发布于开放研究知识图谱（ORKG）中，使其转化为结构化、可查询的知识。在此基础上，我们对比了ORKG的符号化查询与基于大语言模型的查询方式，并论证了在材料科学的可靠神经符号人工智能体系中，经过人工校验的符号层应继续保持其核心支撑地位，而大语言模型应作为符号化基础的补充接口，而非独立的真值来源。