Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence
This addresses the challenge of machine-unfriendly scientific reviews for materials science researchers, though it is incremental as it builds on existing FAIR and neurosymbolic AI concepts.
The paper tackles the problem of inaccessible scientific review insights in materials science by publishing atomic layer deposition/etching review tables as FAIR, machine-actionable comparisons in the Open Research Knowledge Graph, making them structured and queryable. It demonstrates that symbolic querying over ORKG outperforms LLM-based querying, advocating for symbolic knowledge as the backbone of reliable neurosymbolic AI in this domain.
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.