Evaluation of ontology structural metrics based on public repository data
Abstract The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural me...
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| Veröffentlicht in: | Briefings in bioinformatics 2020-03, Vol.21 (2), p.473-485 |
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| creator | Franco, Manuel Vivo, Juana María Quesada-Martínez, Manuel Duque-Ramos, Astrid Fernández-Breis, Jesualdo Tomás |
| description | Abstract
The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus. The evaluation has been done using ontology repositories as corpora. More concretely, we have used 119 ontologies from the OBO Foundry repository and 78 ontologies from AgroPortal. First, we study the correlations between the metrics. Second, we study whether the clusters for a given metric are stable and have a good structure. The results show that the existing correlations are not biasing the evaluation, there are no metrics generating unstable clusterings and all the metrics evaluated provide at least reasonable clustering structure. Furthermore, our work permits to review and suggest the most reliable ontology structural metrics in terms of stability and goodness of their classifications.
Availability: http://sele.inf.um.es/ontology-metrics |
| doi_str_mv | 10.1093/bib/bbz009 |
| format | Article |
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The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus. The evaluation has been done using ontology repositories as corpora. More concretely, we have used 119 ontologies from the OBO Foundry repository and 78 ontologies from AgroPortal. First, we study the correlations between the metrics. Second, we study whether the clusters for a given metric are stable and have a good structure. The results show that the existing correlations are not biasing the evaluation, there are no metrics generating unstable clusterings and all the metrics evaluated provide at least reasonable clustering structure. Furthermore, our work permits to review and suggest the most reliable ontology structural metrics in terms of stability and goodness of their classifications.
Availability: http://sele.inf.um.es/ontology-metrics</description><identifier>ISSN: 1467-5463</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbz009</identifier><identifier>PMID: 30715146</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Clustering ; Correlation analysis ; Evaluation ; Ontology ; Repositories ; Stability analysis</subject><ispartof>Briefings in bioinformatics, 2020-03, Vol.21 (2), p.473-485</ispartof><rights>The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2019</rights><rights>The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</rights><rights>The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c345t-524e21e1a0bee156ba3146d05a94093a7307d54bae34da3c49c73f8942dd69d93</citedby><cites>FETCH-LOGICAL-c345t-524e21e1a0bee156ba3146d05a94093a7307d54bae34da3c49c73f8942dd69d93</cites><orcidid>0000-0002-7558-2880</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,1598,27903,27904</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bib/bbz009$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30715146$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Franco, Manuel</creatorcontrib><creatorcontrib>Vivo, Juana María</creatorcontrib><creatorcontrib>Quesada-Martínez, Manuel</creatorcontrib><creatorcontrib>Duque-Ramos, Astrid</creatorcontrib><creatorcontrib>Fernández-Breis, Jesualdo Tomás</creatorcontrib><title>Evaluation of ontology structural metrics based on public repository data</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Abstract
The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus. The evaluation has been done using ontology repositories as corpora. More concretely, we have used 119 ontologies from the OBO Foundry repository and 78 ontologies from AgroPortal. First, we study the correlations between the metrics. Second, we study whether the clusters for a given metric are stable and have a good structure. The results show that the existing correlations are not biasing the evaluation, there are no metrics generating unstable clusterings and all the metrics evaluated provide at least reasonable clustering structure. Furthermore, our work permits to review and suggest the most reliable ontology structural metrics in terms of stability and goodness of their classifications.
Availability: http://sele.inf.um.es/ontology-metrics</description><subject>Clustering</subject><subject>Correlation analysis</subject><subject>Evaluation</subject><subject>Ontology</subject><subject>Repositories</subject><subject>Stability analysis</subject><issn>1467-5463</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kF1LwzAUhoMobk5v_AESEEGEuqRJ2uVSxtTBwBu9DkmTSkfb1HwI89eb0emFF16dA-fh5T0PAJcY3WPEyVw1aq7UF0L8CEwxLcuMIkaP93tRZowWZALOvN8ilKNygU_BhKASs3SdgvXqU7ZRhsb20NbQ9sG29n0HfXCxCtHJFnYmuKbyUElvdCLgEFXbVNCZwfomWLeDWgZ5Dk5q2XpzcZgz8Pa4el0-Z5uXp_XyYZNVhLKQsZyaHBsskTIGs0JJkopoxCSn6RlZpm6aUSUNoVqSivKqJPWC01zrgmtOZuB2zB2c_YjGB9E1vjJtK3tjoxc5LjnleUFwQq__oFsbXZ_aiZzmHCV7bJGou5GqnPXemVoMrumk2wmMxF6wSILFKDjBV4fIqDqjf9Efowm4GQEbh_-CvgEp8YL5</recordid><startdate>20200323</startdate><enddate>20200323</enddate><creator>Franco, Manuel</creator><creator>Vivo, Juana María</creator><creator>Quesada-Martínez, Manuel</creator><creator>Duque-Ramos, Astrid</creator><creator>Fernández-Breis, Jesualdo Tomás</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SC</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7558-2880</orcidid></search><sort><creationdate>20200323</creationdate><title>Evaluation of ontology structural metrics based on public repository data</title><author>Franco, Manuel ; Vivo, Juana María ; Quesada-Martínez, Manuel ; Duque-Ramos, Astrid ; Fernández-Breis, Jesualdo Tomás</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c345t-524e21e1a0bee156ba3146d05a94093a7307d54bae34da3c49c73f8942dd69d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Clustering</topic><topic>Correlation analysis</topic><topic>Evaluation</topic><topic>Ontology</topic><topic>Repositories</topic><topic>Stability analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Franco, Manuel</creatorcontrib><creatorcontrib>Vivo, Juana María</creatorcontrib><creatorcontrib>Quesada-Martínez, Manuel</creatorcontrib><creatorcontrib>Duque-Ramos, Astrid</creatorcontrib><creatorcontrib>Fernández-Breis, Jesualdo Tomás</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Franco, Manuel</au><au>Vivo, Juana María</au><au>Quesada-Martínez, Manuel</au><au>Duque-Ramos, Astrid</au><au>Fernández-Breis, Jesualdo Tomás</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of ontology structural metrics based on public repository data</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2020-03-23</date><risdate>2020</risdate><volume>21</volume><issue>2</issue><spage>473</spage><epage>485</epage><pages>473-485</pages><issn>1467-5463</issn><eissn>1477-4054</eissn><abstract>Abstract
The development and application of biological ontologies have increased significantly in recent years. These ontologies can be retrieved from different repositories, which do not provide much information about quality aspects of the ontologies. In the past years, some ontology structural metrics have been proposed, but their validity as measurement instrument has not been sufficiently studied to date. In this work, we evaluate a set of reproducible and objective ontology structural metrics. Given the lack of standard methods for this purpose, we have applied an evaluation method based on the stability and goodness of the classifications of ontologies produced by each metric on an ontology corpus. The evaluation has been done using ontology repositories as corpora. More concretely, we have used 119 ontologies from the OBO Foundry repository and 78 ontologies from AgroPortal. First, we study the correlations between the metrics. Second, we study whether the clusters for a given metric are stable and have a good structure. The results show that the existing correlations are not biasing the evaluation, there are no metrics generating unstable clusterings and all the metrics evaluated provide at least reasonable clustering structure. Furthermore, our work permits to review and suggest the most reliable ontology structural metrics in terms of stability and goodness of their classifications.
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| subjects | Clustering Correlation analysis Evaluation Ontology Repositories Stability analysis |
| title | Evaluation of ontology structural metrics based on public repository data |
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