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A materials terminology knowledge graph automatically constructed from text corpus.

Yuwei Zhang1, Fangyi Chen1, Zeyi Liu1

  • 1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.

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|June 7, 2024
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Summary
This summary is machine-generated.

We developed the Materials Genome Engineering Database Knowledge Graph (MGED-KG), a comprehensive resource for materials terminology in Chinese and English. This knowledge graph enhances data sharing and understanding within the materials science community.

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Area of Science:

  • Materials Science
  • Computer Science
  • Natural Language Processing

Background:

  • Unified material knowledge representation is crucial for data sharing in materials science.
  • Existing knowledge graphs for materials terminology lack comprehensive coverage and multilingual support.

Purpose of the Study:

  • To develop a scalable, reusable, and broad-coverage knowledge graph for materials terminology.
  • To create the Materials Genome Engineering Database Knowledge Graph (MGED-KG) for improved data sharing and accessibility.

Main Methods:

  • Utilized natural language processing techniques to automatically construct the knowledge graph from a text corpus.
  • Designed a hierarchical categorization system with 11 principal categories and 235 subcategories.

Main Results:

  • Constructed MGED-KG, the most comprehensive bilingual (Chinese and English) knowledge graph for materials terminology, containing 8,660 terms and explanations.
  • Developed a knowledge web system based on MGED-KG to demonstrate its application in enhancing data sharing.

Conclusions:

  • MGED-KG significantly improves data sharing efficiency through query expansion, term recommendation, and data recommendation.
  • The developed knowledge graph and system offer a powerful tool for the materials science community.