Ontology-conformal recognition of materials entities using language models.

Extracting structured and semantically annotated materials information from unstructured scientific literature is a crucial step toward constructing machine-interpretable knowledge graphs and accelerating data-driven materials research. This is especially important in materials science, which is adversely affected by data scarcity. Data scarcity further motivates employing solutions such as foundation language models for extracting information which can in principle address several subtasks of the information extraction problem in a range of domains without the need of generating costly large-scale annotated datasets for each downstream task. However, foundation language models struggle with tasks like Named Entity Recognition (NER) due to domain-specific terminologies, fine-grained entities, and semantic ambiguity. The issue is even more pronounced when entities must map directly to pre-existing domain ontologies. This work aims to assess whether foundation large language models (LLMs) can successfully perform ontology-conformal NER in the materials mechanics and fatigue domain. Specifically, we present a comparative evaluation of in-context learning (ICL) with foundation models such as GPT-4 against fine-tuned task-specific language models, including MatSciBERT and DeBERTa. The study is performed on two materials fatigue datasets, which contain annotations at a comparatively fine-grained level adhering to the class definitions of a formal ontology to ensure semantic alignment and cross-dataset interoperability. Both datasets cover adjacent domains to assess how well both NER methodologies generalize when presented with typical domain shifts. Task-specific models are shown to significantly outperform general foundation models on an ontology-constrained NER. Our findings reveal a strong dependence on the quality of few-shot demonstrations in ICL to handle domain-shift. The study also highlights the significance of domain-specific pre-training by comparing task-specific models that differ primarily in their pre-training corpus.