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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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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.

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Mendeley readers

The data shown below were compiled from readership statistics for 209 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 180 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 31 15%
Unknown 25 12%
Readers by discipline Count As %
Computer Science 63 30%
Engineering 22 11%
Mathematics 21 10%
Agricultural and Biological Sciences 20 10%
Arts and Humanities 6 3%
Other 48 23%
Unknown 29 14%