# Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence

arXiv: https://arxiv.org/abs/2601.05051 · HTML: https://mlsanity.com/paper/2601.05051/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 59 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [DePlot: One-shot visual language reasoning by plot-to-table translation](https://mlsanity.com/paper/2212.10505/) (2022)
- [Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation](https://mlsanity.com/paper/2412.18537/) (2024)
- [LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs](https://mlsanity.com/paper/2410.06062/) (2024)
- [Benchmarking large language models for materials synthesis: the case of atomic layer deposition](https://mlsanity.com/paper/2412.10477/) (2024)
- [Generate FAIR Literature Surveys with Scholarly Knowledge Graphs](https://mlsanity.com/paper/2006.01747/) (2020)
- [Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services](https://mlsanity.com/paper/2407.00110/) (2024)
- [Creating a Scholarly Knowledge Graph from Survey Article Tables](https://mlsanity.com/paper/2012.00456/) (2020)
- [SmartReviews: Towards Human- and Machine-actionable Representation of Review Articles](https://mlsanity.com/paper/2111.15342/) (2021)

## Abstract (arXiv)

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.