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Supporting the education evidence portal via text mining
The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching m...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2981997/ https://www.ncbi.nlm.nih.gov/pubmed/20643679 http://dx.doi.org/10.1098/rsta.2010.0152 |
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author | Ananiadou, Sophia Thompson, Paul Thomas, James Mu, Tingting Oliver, Sandy Rickinson, Mark Sasaki, Yutaka Weissenbacher, Davy McNaught, John |
author_facet | Ananiadou, Sophia Thompson, Paul Thomas, James Mu, Tingting Oliver, Sandy Rickinson, Mark Sasaki, Yutaka Weissenbacher, Davy McNaught, John |
author_sort | Ananiadou, Sophia |
collection | PubMed |
description | The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching multiple resources to find relevant information. However, the combined content of the websites of interest is still very large (over 500 000 documents and growing). This means that searches using the portal can produce very large numbers of hits. As users often have limited time, they would benefit from enhanced methods of performing searches and viewing results, allowing them to drill down to information of interest more efficiently, without having to sift through potentially long lists of irrelevant documents. The Joint Information Systems Committee (JISC)-funded ASSIST project has produced a prototype web interface to demonstrate the applicability of integrating a number of text-mining tools and methods into the eep, to facilitate an enhanced searching, browsing and document-viewing experience. New features include automatic classification of documents according to a taxonomy, automatic clustering of search results according to similar document content, and automatic identification and highlighting of key terms within documents. |
format | Text |
id | pubmed-2981997 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-29819972010-12-01 Supporting the education evidence portal via text mining Ananiadou, Sophia Thompson, Paul Thomas, James Mu, Tingting Oliver, Sandy Rickinson, Mark Sasaki, Yutaka Weissenbacher, Davy McNaught, John Philos Trans A Math Phys Eng Sci Articles The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching multiple resources to find relevant information. However, the combined content of the websites of interest is still very large (over 500 000 documents and growing). This means that searches using the portal can produce very large numbers of hits. As users often have limited time, they would benefit from enhanced methods of performing searches and viewing results, allowing them to drill down to information of interest more efficiently, without having to sift through potentially long lists of irrelevant documents. The Joint Information Systems Committee (JISC)-funded ASSIST project has produced a prototype web interface to demonstrate the applicability of integrating a number of text-mining tools and methods into the eep, to facilitate an enhanced searching, browsing and document-viewing experience. New features include automatic classification of documents according to a taxonomy, automatic clustering of search results according to similar document content, and automatic identification and highlighting of key terms within documents. The Royal Society Publishing 2010-08-28 /pmc/articles/PMC2981997/ /pubmed/20643679 http://dx.doi.org/10.1098/rsta.2010.0152 Text en © 2010 The Royal Society http://creativecommons.org/licenses/by/2.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Ananiadou, Sophia Thompson, Paul Thomas, James Mu, Tingting Oliver, Sandy Rickinson, Mark Sasaki, Yutaka Weissenbacher, Davy McNaught, John Supporting the education evidence portal via text mining |
title | Supporting the education evidence portal via text mining |
title_full | Supporting the education evidence portal via text mining |
title_fullStr | Supporting the education evidence portal via text mining |
title_full_unstemmed | Supporting the education evidence portal via text mining |
title_short | Supporting the education evidence portal via text mining |
title_sort | supporting the education evidence portal via text mining |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2981997/ https://www.ncbi.nlm.nih.gov/pubmed/20643679 http://dx.doi.org/10.1098/rsta.2010.0152 |
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