Conference proceeding
Knowledge Graph-Empowered Materials Discovery
2021 IEEE International Conference on Big Data (Big Data), pp 4628-4632
15 Dec 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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Details
- Title
- Knowledge Graph-Empowered Materials Discovery
- Creators
- Xintong Zhao - Drexel UniversityJane Greenberg - Drexel UniversityScott McClellan - Drexel UniversityYong-Jie Hu - Drexel UniversitySteven Lopez - Northeastern UniversitySemion K Saikin - KebotixXiaohua Hu - Drexel UniversityYuan An - Drexel University
- Publication Details
- 2021 IEEE International Conference on Big Data (Big Data), pp 4628-4632
- Conference
- 2021 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Number of pages
- 1
- Grant note
- Office of Advanced Cyberinfrastructure (10.13039/100000105) National Science Foundation (10.13039/100000001)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science; Materials Science and Engineering
- Web of Science ID
- WOS:000800559504108
- Scopus ID
- 2-s2.0-85125359332
- Other Identifier
- 991019169625304721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Computer Science, Theory & Methods