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Title: Knowledge Graph-Empowered Materials Discovery
In this position paper, we describe research on knowledge graph-empowered materials science prediction and discovery. The research consists of several key components including ontology mapping, materials data annotation, and information extraction from unstructured scholarly articles. We argue that although big data generated by simulations and experiments have motivated and accelerated the data-driven science, the distribution and heterogeneity of materials science-related big data hinders major advancements in the field. Knowledge graphs, as semantic hubs, integrate disparate data and provide a feasible solution to addressing this challenge. We design a knowledge-graph based approach for data discovery, extraction, and integration in materials science.  more » « less
Award ID(s):
1940239
PAR ID:
10393958
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
2021 IEEE International Conference on Big Data (Big Data)
Page Range / eLocation ID:
4628 to 4632
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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