The article outlines a methodology for structuring material data into triples organized by core labels: Formula, Name, and Acronym. By prioritizing these labels, the authors enhance the relational understanding of materials. This structured data is then incorporated into a Functional Material Knowledge Graph (FMKG), powered by Neo4j, facilitating advanced querying and subgraph matching. This approach improves the management of complex material relationships and supports researchers in material science for better data analysis and retrieval.
The structured inference results into triples enable a clear relationship description between materials by defining core labels, which allows for improved data retrieval and analysis.
Employing a graph database like Neo4j enhances the capabilities of managing material relations, enabling efficient subgraph matching alongside standard querying methods.
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