期刊
ADVANCED ENERGY MATERIALS
卷 11, 期 16, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/aenm.202003580
关键词
lithium iron phosphate; machine learning; matching dependencies; materials knowledge graphs
类别
资金
- National Key R&D Program of China [2016YFB0700600]
- Chemistry and Chemical Engineering Guangdong Laboratory [1922018]
- Shenzhen Science and Technology Research Grant [JCYJ20200109140416788]
The article introduces a materials knowledge graph named MatKG, which addresses issues in materials science research through the construction of the knowledge graph and author disambiguation. It demonstrates the application of MatKG in tracking research trends in LiFePO4 and automatically documenting milestones achieved.
Due to the recent innovations in computer technology, the emerging field of materials informatics has now become a catalyst for a revolution of the research paradigm in materials science. Knowledge graphs, which provide support for knowledge management, are able to collectively capture the scientific knowledge from the vast collection of research articles and accomplish the automatic recognition of the relationships between entities. In this work, a materials knowledge graph, named MatKG, is constructed, which establishes a unique correspondence between subjects and objects in the materials science area. An emphasis is placed on the disambiguation of authors, addressed by a deduplication model based on machine learning and matching dependencies algorithms. Specifically, MatKG is applied to perform tracking on research trends in the study of LiFePO4 and to automatically chronicle the milestones achieved so far. It is believed that MatKG can serve as a versatile research platform for amalgamating and refining the scientific knowledge of materials in a variety of subfields and intersectional domains.
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