Evidence map›Paper›PMID 20529942›Full record

ArticleBioinformatics (Oxford, England)2010

Semi-automated ontology generation within OBO-Edit.

Thomas Wächter, Michael Schroeder

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Formalizing biomedical concepts from textual definitions.Journal of biomedical semantics · 2015
    Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Predicting the extension of biomedical ontologies.PLoS computational biology · 2012
    Article
  10. 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

2 authors.

Thomas WächterBiotechnology Center, Technische Universität Dresden, 01062 Dresden, Germany. thomas.waechter@biotec.tu-dresden.de
Michael Schroeder

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationOntologies and taxonomies have proven highly beneficial for biocuration. The Open Biomedical Ontology (OBO) Foundry alone lists over 90 ontologies mainly built with OBO-Edit. Creating and maintaining such ontologies is a labour-intensive, difficult, manual process. Automating parts of it is of great importance for the further development of ontologies and for biocuration.

resultsWe have developed the Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG), a system which supports the creation and extension of OBO ontologies by semi-automatically generating terms, definitions and parent-child relations from text in PubMed, the web and PDF repositories. DOG4DAG is seamlessly integrated into OBO-Edit. It generates terms by identifying statistically significant noun phrases in text. For definitions and parent-child relations it employs pattern-based web searches. We systematically evaluate each generation step using manually validated benchmarks. The term generation leads to high-quality terms also found in manually created ontologies. Up to 78% of definitions are valid and up to 54% of child-ancestor relations can be retrieved. There is no other validated system that achieves comparable results. By combining the prediction of high-quality terms, definitions and parent-child relations with the ontology editor OBO-Edit we contribute a thoroughly validated tool for all OBO ontology engineers. AVAILABILITY: DOG4DAG is available within OBO-Edit 2.1 at http://www.oboedit.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

SoftwareVocabulary, ControlledComputational BiologyDatabase Management SystemsInformation Storage and RetrievalInternetProgramming LanguagesUser-Computer Interface

Identifiers

PMID20529942
PMCPMC2881373

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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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.