A Domain Expert-Guided Knowledge Mining Framework for Exploring Materials Structure-Property Relationships

doi:10.15541/jim20260265

Abstract

Abstract: Knowledge mining enables the automated extraction of latent scientific principles from large-scale materials science literature, of which performance is confined to the annotated data quality. However, current annotation paradigms usually fail to describe key microscopic mechanisms. Here, we propose a domain expert-guided knowledge mining framework, involving ontology-level constraints for standardizing composition-structure-property mapping logic representations, data insight evaluation strategy for quantifying data learnability to control annotation quality and graph analytics strategy based on Neo4j for visualizing large-scale triples and presenting knowledge. We demonstrate its application to building the knowledge graph of composite solid electrolytes (CSEs) with complex ionic transport mechanisms. Benefiting from the domain knowledge-incorporated dataset, a trained knowledge mining model automatizes the entity relationship extraction from a curated corpus of 837 publications and then a knowledge graph with over 20,000 nodes is constructed. Results indicate that the enhancement of CSEs in ionic conductivity and mechanical properties is explained through polymer chain segment motion, continuous conduction pathways and interfacial mechanical reinforcement, arising from fillers’ intrinsic characteristics and diverse spatial morphologies. This work not only enables ‌standardized‌ transformation of knowledge from unstructured text into high-density visualized information, but provides a scalable framework to automatize knowledge mining for accelerating materials design. The datasets in this article are listed in Science Data Bank at https://www.doi.org/10.57760/sciencedb.jim.00094.

Key words: knowledge mining, knowledge graph, domain expert guidance, composite solid-state electrolyte, composition-structure-property relationships

CLC Number:

TP181
TB3

ZUO Yanlin, YANG Zhengwei, SHENG Haoqiang, WANG Da, SHI Siqi. A Domain Expert-Guided Knowledge Mining Framework for Exploring Materials Structure-Property Relationships[J]. Journal of Inorganic Materials, DOI: 10.15541/jim20260265.

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