Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model

Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges for efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experimental approaches, further complicate rapid innovat...

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Published inarXiv.org
Main Authors Ye, Yanpeng, Ren, Jie, Wang, Shaozhou, Wan, Yuwei, Wang, Haofen, Razzak, Imran, Hoex, Bram, Xie, Tong, Zhang, Wenjie
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.09.2024
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Artificial intelligence
Functional materials
Knowledge management
Knowledge representation
Large language models
Management systems
Materials science
Natural language processing
Unstructured data
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Summary:Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges for efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experimental approaches, further complicate rapid innovation. Addressing these challenges, the integration of artificial intelligence with materials science has opened avenues for accelerating the discovery process, though it also demands precise annotation, data extraction, and traceability of information. To tackle these issues, this article introduces the Materials Knowledge Graph (MKG), which utilizes advanced natural language processing techniques, integrated with large language models to extract and systematically organize a decade's worth of high-quality research into structured triples, contains 162,605 nodes and 731,772 edges. MKG categorizes information into comprehensive labels such as Name, Formula, and Application, structured around a meticulously designed ontology, thus enhancing data usability and integration. By implementing network-based algorithms, MKG not only facilitates efficient link prediction but also significantly reduces reliance on traditional experimental methods. This structured approach not only streamlines materials research but also lays the groundwork for more sophisticated science knowledge graphs.
ISSN:2331-8422