Evidence map›Paper›PMID 38849436›Full record

ArticleScientific data2024

A materials terminology knowledge graph automatically constructed from text corpus.

Yuwei Zhang, Fangyi Chen, Zeyi Liu, Yunzhuo Ju, Dongliang Cui, Jinyi Zhu, Xue Jiang, Xi Guo, Jie He, Lei Zhang and 2 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yuwei Zhang *School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Fangyi Chen *School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Zeyi Liu *School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.ORCID http://orcid.org/0000-0002-5691-6849
Yunzhuo JuSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Dongliang CuiSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Jinyi ZhuSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Xue JiangBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China. jiangxue@ustb.edu.cn.ORCID http://orcid.org/0000-0001-7441-0828
Xi GuoSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China. xiguo@ustb.edu.cn.
Jie HeSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China. hejie@ustb.edu.cn.
Lei ZhangBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
Xiaotong ZhangSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
Yanjing SuBeijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52201061National Natural Science Foundation of China (National Science Foundation of China) U22A20106
6 · The paper itself

Abstract

A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.

Identifiers

PMID38849436
PMCPMC11161478

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.