Using an Ontology-Based Approach to Handle Author Affiliations in a Large Biomedical Citation Database.

Haixia Sun, Junlian Li, Yingjie Wu, Lei Wang, Kin Wah Fung

Abstract read

In one paragraph

Article in Studies in health technology and informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Haixia SunInstitute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.

Junlian LiInstitute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.

Yingjie WuInstitute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.

Lei WangInstitute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.

Kin Wah FungNational Library of Medicine, Bethesda, MD, USA.

Funding

Clinical and Informatics Research in Medical TerminologiesZIALM010013 · NLM · NATIONAL LIBRARY OF MEDICINE · PI FUNG, KIN WAH · 2018 to 2020

$1.6M

Intramural NIH HHS Z99 LM999999

6 · The paper itself

Abstract

To handle differences in affiliation names submitted with biomedical journal articles, we build an affiliation knowledge base named Authority File for Affiliations (AFA) based on ontology principles. There are currently 113,700 affiliation concepts with about 583,700 affiliation names. The AFA becomes an essential tool in managing citation information and data analysis.

Indexed as

AuthorshipDatabases, FactualKnowledge BasesMetadataBiological OntologiesDatabasesInformation ManagementOrganizational Affiliation

Identifiers

PMID29295419

PMCPMC12139005

What OpenQuestion holds

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LicenceCC BY-NC

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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.