ArticlePLoS computational biology2011
Benchmarking ontologies: bigger or better?
Article in PLoS computational biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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Who cites it
8 citing papers in PubMed.
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- Quantifying the impact and extent of undocumented biomedical synonymy.PLoS computational biology · 2014Article
- Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters.BMC bioinformatics · 2014Article
- Evaluation of research in biomedical ontologies.Briefings in bioinformatics · 2013Article
- Article
- Quantitative comparison of mapping methods between Human and Mammalian Phenotype Ontology.Journal of biomedical semantics · 2012Article
- Extending ontologies by finding siblings using set expansion techniques.Bioinformatics (Oxford, England) · 2012Article
- War of ontology worlds: mathematics, computer code, or Esperanto?PLoS computational biology · 2011Article
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Authors and funding
5 authors.
Funding
Abstract
A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them.
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Registered trials
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.