MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities

TL;DR

MaterioMiner is a detailed dataset linking materials science ontologies with literature text, enabling training and benchmarking of neurosymbolic models for extracting complex process-structure-property relationships.

Contribution

The paper introduces the MaterioMiner dataset with fine-granular annotations linking ontologies to literature, supporting neurosymbolic model training and automated knowledge extraction in materials science.

Findings

High annotation consistency among raters

Successful fine-tuning of pre-trained models for entity recognition

Dataset facilitates materials language model benchmarking

Abstract

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.