Author ORCID Identifier
Document Type
Poster
Publication Date
1-14-2026
Abstract
Combining data from multiple sources is crucial for efficient knowledge aggregation in materials data science. FAIR data from ontology and Linked Data principles enable this. Semantic data management streamlines data exchange and aggregation, ensuring information is available and extractable. FAIRLinked and GraphDB provide solutions for consolidating, hosting, and extracting meaningful insight from multimodal data.
Keywords
FAIR, knowledge graph, semantic data management, materials data science
Language
English
Rights
© The Author(s). This is an Open Access work distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Henrikson, Kyle R.; Tran, Van D.; Francis, Meredith; Giammattei, Isabella; Tran, Quynh D.; Bruckman, Laura S.; Barcelos, Erika I.; and French, Roger H., "Reproducible Semantic Data Management Workflow for Materials Data Science: Generating Knowledge Graphs with Robust FAIRifcation Pipelines" (2026). Student Scholarship. 680.
https://commons.case.edu/studentworks/680