Evidence map›Paper›PMID 26988001›Full record

ArticleJournal of biomedical informatics2016

Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies.

Christopher Ochs, Zhe He, Ling Zheng, James Geller, Yehoshua Perl, George Hripcsak, Mark A Musen

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Missing lateral relationships in top-level concepts of an ontology.BMC medical informatics and decision making · 2020
    Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. OWL-NETS: Transforming OWL Representations for Improved Network Inference.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018
    Article
  7. Article
  8. An empirical analysis of ontology reuse in BioPortal.Journal of biomedical informatics · 2017
    Article
  9. Article
  10. Taxonomy-Based Approaches to Quality Assurance of Ontologies.Journal of healthcare engineering · 2017
    Article
  11. Introducing the Big Knowledge to Use (BK2U) challenge.Annals of the New York Academy of Sciences · 2017
    Review
  12. 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

7 authors.

Christopher OchsComputer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA. Electronic address: cro3@njit.edu.
Zhe HeSchool of Information, Florida State University, Tallahassee, FL 32306, USA.
Ling ZhengComputer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
James GellerComputer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
Yehoshua PerlComputer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
George HripcsakDepartment of Biomedical Informatics, Columbia University, New York, NY 10027, USA.
Mark A MusenStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, USA.

Funding

STANFORD COMPOSITE(RMI)U54HG004028 · NHGRI · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2005 to 2014
$36.2M
A family-based framework of quality assurance for biomedical ontologiesR01CA190779 · NCI · NEW JERSEY INSTITUTE OF TECHNOLOGY · PI PERL, YEHOSHUA · 2015 to 2017
$1.8M
NCI NIH HHS R01 CA190779NHGRI NIH HHS U54 HG004028
6 · The paper itself

Abstract

An Abstraction Network is a compact summary of an ontology's structure and content. In previous research, we showed that Abstraction Networks support quality assurance (QA) of biomedical ontologies. The development of an Abstraction Network and its associated QA methodologies, however, is a labor-intensive process that previously was applicable only to one ontology at a time. To improve the efficiency of the Abstraction-Network-based QA methodology, we introduced a QA framework that uses uniform Abstraction Network derivation techniques and QA methodologies that are applicable to whole families of structurally similar ontologies. For the family-based framework to be successful, it is necessary to develop a method for classifying ontologies into structurally similar families. We now describe a structural meta-ontology that classifies ontologies according to certain structural features that are commonly used in the modeling of ontologies (e.g., object properties) and that are important for Abstraction Network derivation. Each class of the structural meta-ontology represents a family of ontologies with identical structural features, indicating which types of Abstraction Networks and QA methodologies are potentially applicable to all of the ontologies in the family. We derive a collection of 81 families, corresponding to classes of the structural meta-ontology, that enable a flexible, streamlined family-based QA methodology, offering multiple choices for classifying an ontology. The structure of 373 ontologies from the NCBO BioPortal is analyzed and each ontology is classified into multiple families modeled by the structural meta-ontology.

Indexed as

Biological OntologiesQuality ControlAbstraction networkFamily-based ontology quality assuranceOntology classificationStructural meta-ontology

Identifiers

PMID26988001
PMCPMC4893909

What OpenQuestion holds

Textmetadata
LicenceTDM
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.