MatKG: An autonomously generated knowledge graph in Material Science.

Journal

Scientific data

ISSN: 2052-4463

Titre abrégé: Sci Data

Pays: England

ID NLM: 101640192

Informations de publication

Date de publication:
17 Feb 2024

Historique:

received: 21 11 2023

accepted: 01 02 2024

medline: 19 2 2024

pubmed: 18 2 2024

entrez: 17 2 2024

Statut: epublish

Résumé

In this paper, we present MatKG, a knowledge graph in materials science that offers a repository of entities and relationships extracted from scientific literature. Using advanced natural language processing techniques, MatKG includes an array of entities, including materials, properties, applications, characterization and synthesis methods, descriptors, and symmetry phase labels. The graph is formulated based on statistical metrics, encompassing over 70,000 entities and 5.4 million unique triples. To enhance accessibility and utility, we have serialized MatKG in both CSV and RDF formats and made these, along with the code base, available to the research community. As the largest knowledge graph in materials science to date, MatKG provides structured organization of domain-specific data. Its deployment holds promise for various applications, including material discovery, recommendation systems, and advanced analytics.

Identifiants

DOI: 10.1038/s41597-024-03039-z PMID: 38368452 PMC: PMC10874416

pubmed: 38368452

doi: 10.1038/s41597-024-03039-z

pii: 10.1038/s41597-024-03039-z

pmc: PMC10874416

doi:

Types de publication

Dataset Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

217

Informations de copyright

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