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War of Ontology Worlds: Mathematics, Computer Code, or Esperanto?

Overview of attention for article published in PLoS Computational Biology, September 2011
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Title
War of Ontology Worlds: Mathematics, Computer Code, or Esperanto?
Published in
PLoS Computational Biology, September 2011
DOI 10.1371/journal.pcbi.1002191
Pubmed ID
Authors

Andrey Rzhetsky, James A. Evans

Abstract

The use of structured knowledge representations-ontologies and terminologies-has become standard in biomedicine. Definitions of ontologies vary widely, as do the values and philosophies that underlie them. In seeking to make these views explicit, we conducted and summarized interviews with a dozen leading ontologists. Their views clustered into three broad perspectives that we summarize as mathematics, computer code, and Esperanto. Ontology as mathematics puts the ultimate premium on rigor and logic, symmetry and consistency of representation across scientific subfields, and the inclusion of only established, non-contradictory knowledge. Ontology as computer code focuses on utility and cultivates diversity, fitting ontologies to their purpose. Like computer languages C++, Prolog, and HTML, the code perspective holds that diverse applications warrant custom designed ontologies. Ontology as Esperanto focuses on facilitating cross-disciplinary communication, knowledge cross-referencing, and computation across datasets from diverse communities. We show how these views align with classical divides in science and suggest how a synthesis of their concerns could strengthen the next generation of biomedical ontologies.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 9%
Brazil 5 4%
Germany 4 3%
Netherlands 4 3%
United Kingdom 4 3%
France 2 1%
Canada 2 1%
Russia 2 1%
Mexico 2 1%
Other 10 7%
Unknown 90 66%

Demographic breakdown

Readers by professional status Count As %
Researcher 47 34%
Student > Ph. D. Student 19 14%
Professor 16 12%
Student > Master 11 8%
Other 10 7%
Other 27 20%
Unknown 7 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 27%
Computer Science 33 24%
Medicine and Dentistry 12 9%
Social Sciences 6 4%
Business, Management and Accounting 5 4%
Other 35 26%
Unknown 9 7%