Literature DB >> 22554701

Measuring the level of activity in community built bio-ontologies.

James Malone1, Robert Stevens.   

Abstract

In this paper we explore the measurement of activity in ontology projects as an aspect of community ontology building. When choosing whether to use an ontology or whether to participate in its development, having some knowledge of how actively that ontology is developed is an important issue. Our knowledge of biology grows and changes and an ontology must adapt to keep pace with those changes and also adapt with respect to other ontologies and organisational principles. In essence, we need to know if there is an 'active' community involved with a project or whether a given ontology is inactive or moribund. We explore the use of additions, deletions and changes to ontology files, the regularity and frequency of releases, and the number of ontology repository updates to an ontology as the basis for measuring activity in an ontology. We present our results of this study, which show a dramatic range of activity across some of the more prominent community ontologies, illustrating very active and mature efforts through to those which appear to have become dormant for a number of possible reasons. We show that global activity within the community has remained at a similar level over the last 2 years. Measuring additions, deletions and changes, together with release frequency, appear to be useful metrics of activity and useful pointers towards future behaviour. Measuring who is making edits to ontologies is harder to capture; this raises issues of record keeping in ontology projects and in micro-credit, although we have identified one ontologist that appears influential across many community efforts; a Super-Ontologist. We also discuss confounding factors in our activity metric and discuss how it can be improved and adopted as an assessment criterion for community ontology development. Overall, we show that it is possible to objectively measure the activity in an ontology and to make some prediction about future activity.
Copyright © 2012 Elsevier Inc. All rights reserved.

Mesh:

Year:  2012        PMID: 22554701     DOI: 10.1016/j.jbi.2012.04.002

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  6 in total

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3.  Ten Simple Rules for Selecting a Bio-ontology.

Authors:  James Malone; Robert Stevens; Simon Jupp; Tom Hancocks; Helen Parkinson; Cath Brooksbank
Journal:  PLoS Comput Biol       Date:  2016-02-11       Impact factor: 4.475

4.  DMTO: a realistic ontology for standard diabetes mellitus treatment.

Authors:  Shaker El-Sappagh; Daehan Kwak; Farman Ali; Kyung-Sup Kwak
Journal:  J Biomed Semantics       Date:  2018-02-06

5.  Measuring the evolution of ontology complexity: the gene ontology case study.

Authors:  Olivier Dameron; Charles Bettembourg; Nolwenn Le Meur
Journal:  PLoS One       Date:  2013-10-11       Impact factor: 3.240

6.  Webulous and the Webulous Google Add-On--a web service and application for ontology building from templates.

Authors:  Simon Jupp; Tony Burdett; Danielle Welter; Sirarat Sarntivijai; Helen Parkinson; James Malone
Journal:  J Biomed Semantics       Date:  2016-04-01
  6 in total

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