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Ologs: A Categorical Framework for Knowledge Representation

Overview of attention for article published in PLOS ONE, January 2012
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

blogs
2 blogs
twitter
27 X users
patent
1 patent
facebook
1 Facebook page
wikipedia
5 Wikipedia pages
googleplus
2 Google+ users
q&a
1 Q&A thread

Citations

dimensions_citation
86 Dimensions

Readers on

mendeley
210 Mendeley
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Title
Ologs: A Categorical Framework for Knowledge Representation
Published in
PLOS ONE, January 2012
DOI 10.1371/journal.pone.0024274
Pubmed ID
Authors

David I. Spivak, Robert E. Kent

Abstract

In this paper we introduce the olog, or ontology log, a category-theoretic model for knowledge representation (KR). Grounded in formal mathematics, ologs can be rigorously formulated and cross-compared in ways that other KR models (such as semantic networks) cannot. An olog is similar to a relational database schema; in fact an olog can serve as a data repository if desired. Unlike database schemas, which are generally difficult to create or modify, ologs are designed to be user-friendly enough that authoring or reconfiguring an olog is a matter of course rather than a difficult chore. It is hoped that learning to author ologs is much simpler than learning a database definition language, despite their similarity. We describe ologs carefully and illustrate with many examples. As an application we show that any primitive recursive function can be described by an olog. We also show that ologs can be aligned or connected together into a larger network using functors. The various methods of information flow and institutions can then be used to integrate local and global world-views. We finish by providing several different avenues for future research.

X Demographics

X Demographics

The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 210 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 12 6%
Japan 4 2%
United Kingdom 2 <1%
China 2 <1%
Norway 1 <1%
Canada 1 <1%
New Zealand 1 <1%
Mexico 1 <1%
Australia 1 <1%
Other 4 2%
Unknown 181 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 49 23%
Student > Ph. D. Student 37 18%
Other 30 14%
Student > Master 25 12%
Student > Bachelor 12 6%
Other 32 15%
Unknown 25 12%
Readers by discipline Count As %
Computer Science 63 30%
Engineering 22 10%
Mathematics 21 10%
Agricultural and Biological Sciences 20 10%
Arts and Humanities 6 3%
Other 49 23%
Unknown 29 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 48. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 February 2024.
All research outputs
#888,199
of 25,791,495 outputs
Outputs from PLOS ONE
#11,580
of 224,873 outputs
Outputs of similar age
#5,225
of 255,276 outputs
Outputs of similar age from PLOS ONE
#134
of 3,346 outputs
Altmetric has tracked 25,791,495 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 224,873 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 255,276 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 3,346 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.