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Visualizing the topical structure of the medical sciences: a self-organizing map approach
We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM) method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1) little is known about how to deal with...
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Published in: | PloS one 2013-03, Vol.8 (3), p.e58779-e58779 |
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description | We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM) method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1) little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2) post-training geometric and semiotic transformations of the SOM tend to be limited, and (3) no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues.
Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS) techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains.
Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid. |
doi_str_mv | 10.1371/journal.pone.0058779 |
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Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS) techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains.
Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0058779</identifier><identifier>PMID: 23554924</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Artificial Intelligence ; Bibliometrics ; Biomedical Research - statistics & numerical data ; Cartography ; Computational neuroscience ; Computer Science ; Data mining ; Datasets ; Finite element method ; Geographic information systems ; Geography ; Knowledge ; Library and information science ; Mapping ; Mathematics ; Medical Informatics - methods ; Medicine ; Models, Theoretical ; Neurons ; Parallel processing ; Publications - statistics & numerical data ; Remote sensing ; Satellite navigation systems ; Scholarly communication ; Schools of library and information science ; Science Policy ; Self organizing maps ; Social and Behavioral Sciences ; Studies ; Two dimensional models ; Visualization</subject><ispartof>PloS one, 2013-03, Vol.8 (3), p.e58779-e58779</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Skupin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2013 Skupin et al 2013 Skupin et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-e395b6d9ad32f99d1a2fee5e662e2110434f5d5b5d1e30a761194bcc998889b53</citedby><cites>FETCH-LOGICAL-c692t-e395b6d9ad32f99d1a2fee5e662e2110434f5d5b5d1e30a761194bcc998889b53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1330898311/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1330898311?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23554924$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Dehmer, Matthias</contributor><creatorcontrib>Skupin, André</creatorcontrib><creatorcontrib>Biberstine, Joseph R</creatorcontrib><creatorcontrib>Börner, Katy</creatorcontrib><title>Visualizing the topical structure of the medical sciences: a self-organizing map approach</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM) method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1) little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2) post-training geometric and semiotic transformations of the SOM tend to be limited, and (3) no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues.
Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS) techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains.
Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Skupin, André</au><au>Biberstine, Joseph R</au><au>Börner, Katy</au><au>Dehmer, Matthias</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Visualizing the topical structure of the medical sciences: a self-organizing map approach</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2013-03-12</date><risdate>2013</risdate><volume>8</volume><issue>3</issue><spage>e58779</spage><epage>e58779</epage><pages>e58779-e58779</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>We implement a high-resolution visualization of the medical knowledge domain using the self-organizing map (SOM) method, based on a corpus of over two million publications. While self-organizing maps have been used for document visualization for some time, (1) little is known about how to deal with truly large document collections in conjunction with a large number of SOM neurons, (2) post-training geometric and semiotic transformations of the SOM tend to be limited, and (3) no user studies have been conducted with domain experts to validate the utility and readability of the resulting visualizations. Our study makes key contributions to all of these issues.
Documents extracted from Medline and Scopus are analyzed on the basis of indexer-assigned MeSH terms. Initial dimensionality is reduced to include only the top 10% most frequent terms and the resulting document vectors are then used to train a large SOM consisting of over 75,000 neurons. The resulting two-dimensional model of the high-dimensional input space is then transformed into a large-format map by using geographic information system (GIS) techniques and cartographic design principles. This map is then annotated and evaluated by ten experts stemming from the biomedical and other domains.
Study results demonstrate that it is possible to transform a very large document corpus into a map that is visually engaging and conceptually stimulating to subject experts from both inside and outside of the particular knowledge domain. The challenges of dealing with a truly large corpus come to the fore and require embracing parallelization and use of supercomputing resources to solve otherwise intractable computational tasks. Among the envisaged future efforts are the creation of a highly interactive interface and the elaboration of the notion of this map of medicine acting as a base map, onto which other knowledge artifacts could be overlaid.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>23554924</pmid><doi>10.1371/journal.pone.0058779</doi><tpages>e58779</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Artificial Intelligence Bibliometrics Biomedical Research - statistics & numerical data Cartography Computational neuroscience Computer Science Data mining Datasets Finite element method Geographic information systems Geography Knowledge Library and information science Mapping Mathematics Medical Informatics - methods Medicine Models, Theoretical Neurons Parallel processing Publications - statistics & numerical data Remote sensing Satellite navigation systems Scholarly communication Schools of library and information science Science Policy Self organizing maps Social and Behavioral Sciences Studies Two dimensional models Visualization |
title | Visualizing the topical structure of the medical sciences: a self-organizing map approach |
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