Evaluating the botanical coverage of PATO using an unsupervised learning algorithm

Alyssa Janning; Hong Cui

doi:10.1145/2132176.2132265

Evaluating the botanical coverage of PATO using an unsupervised learning algorithm

Alyssa Janning, Hong Cui

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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

In this paper, we explore issues in adopting PATO as a standard phenotypic quality ontology for the biological community. Using CharaParser's unsupervised learning algorithm and the Stanford Parser, we extract morphological descriptions from Flora of North America to be matched to terms in PATO. Using the resulting data, we examine PATO's coverage of botanically interesting terms in order to find gaps and to determine accuracy. To maintain PATO's neutrality, we recommend that term definitions be reevaluated and propose that complimentary ontologies be enhanced to close any outstanding gaps in terminology.

Original language	English (US)
Title of host publication	Proceedings of the 2012 iConference
Subtitle of host publication	Culture, Design, Society, iConference 2012
Pages	504-505
Number of pages	2
DOIs	https://doi.org/10.1145/2132176.2132265
State	Published - 2012
Event	2012 iConference: Culture, Design, Society, iConference 2012 - Toronto, ON, Canada Duration: Feb 7 2012 → Feb 10 2012

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	2012 iConference: Culture, Design, Society, iConference 2012
Country/Territory	Canada
City	Toronto, ON
Period	2/7/12 → 2/10/12

Keywords

Flora of North America
PATO
Stanford Parser
botany
ontologies
open biological ontologies
unsupervised learning

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/2132176.2132265

Cite this

Janning, A & Cui, H 2012, Evaluating the botanical coverage of PATO using an unsupervised learning algorithm. in Proceedings of the 2012 iConference: Culture, Design, Society, iConference 2012. ACM International Conference Proceeding Series, pp. 504-505, 2012 iConference: Culture, Design, Society, iConference 2012, Toronto, ON, Canada, 2/7/12. https://doi.org/10.1145/2132176.2132265

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abstract = "In this paper, we explore issues in adopting PATO as a standard phenotypic quality ontology for the biological community. Using CharaParser's unsupervised learning algorithm and the Stanford Parser, we extract morphological descriptions from Flora of North America to be matched to terms in PATO. Using the resulting data, we examine PATO's coverage of botanically interesting terms in order to find gaps and to determine accuracy. To maintain PATO's neutrality, we recommend that term definitions be reevaluated and propose that complimentary ontologies be enhanced to close any outstanding gaps in terminology.",

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AB - In this paper, we explore issues in adopting PATO as a standard phenotypic quality ontology for the biological community. Using CharaParser's unsupervised learning algorithm and the Stanford Parser, we extract morphological descriptions from Flora of North America to be matched to terms in PATO. Using the resulting data, we examine PATO's coverage of botanically interesting terms in order to find gaps and to determine accuracy. To maintain PATO's neutrality, we recommend that term definitions be reevaluated and propose that complimentary ontologies be enhanced to close any outstanding gaps in terminology.

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Evaluating the botanical coverage of PATO using an unsupervised learning algorithm

Abstract

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Keywords

ASJC Scopus subject areas

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