  Structured science summaries or research contributions using properties or
dimensions beyond traditional keywords enhances science findability. Current
methods, such as those used by the Open Research Knowledge Graph (ORKG),
involve manually curating properties to describe research papers' contributions
in a structured manner, but this is labor-intensive and inconsistent between
the domain expert human curators. We propose using Large Language Models (LLMs)
to automatically suggest these properties. However, it's essential to assess
the readiness of LLMs like GPT-3.5, Llama 2, and Mistral for this task before
application. Our study performs a comprehensive comparative analysis between
ORKG's manually curated properties and those generated by the aforementioned
state-of-the-art LLMs. We evaluate LLM performance through four unique
perspectives: semantic alignment and deviation with ORKG properties,
fine-grained properties mapping accuracy, SciNCL embeddings-based cosine
similarity, and expert surveys comparing manual annotations with LLM outputs.
These evaluations occur within a multidisciplinary science setting. Overall,
LLMs show potential as recommendation systems for structuring science, but
further finetuning is recommended to improve their alignment with scientific
tasks and mimicry of human expertise.
