MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities
This provides a resource for training and benchmarking materials language models, automated ontology construction, and knowledge graph generation, addressing a domain-specific need in materials science.
The authors tackled the lack of datasets for neurosymbolic models by creating MaterioMiner, a fine-grained annotated dataset linking a materials mechanics ontology to text, resulting in 2191 entities across 179 classes from four publications. They demonstrated feasibility through annotation consistency analysis and named-entity recognition model fine-tuning.
While large language models learn sound statistical representations of the language and information therein, ontologies are symbolic knowledge representations that can complement the former ideally. Research at this critical intersection relies on datasets that intertwine ontologies and text corpora to enable training and comprehensive benchmarking of neurosymbolic models. We present the MaterioMiner dataset and the linked materials mechanics ontology where ontological concepts from the mechanics of materials domain are associated with textual entities within the literature corpus. Another distinctive feature of the dataset is its eminently fine-granular annotation. Specifically, 179 distinct classes are manually annotated by three raters within four publications, amounting to a total of 2191 entities that were annotated and curated. Conceptual work is presented for the symbolic representation of causal composition-process-microstructure-property relationships. We explore the annotation consistency between the three raters and perform fine-tuning of pre-trained models to showcase the feasibility of named-entity recognition model training. Reusing the dataset can foster training and benchmarking of materials language models, automated ontology construction, and knowledge graph generation from textual data.