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| Title: | Enhancing Search and Browse Using Automated Clustering of Subject Metadata |
| Authors: | Hagedorn, Kat
Chapman, Suzanne
Newman, David |
| Keywords: | clustering
metadata
digital libraries
search
browse
algorithms
Open Archives Initiative |
| Issue Date: | Jul-2007 |
| Publisher: | Corporation for National Research Initiatives |
| Citation: | D-Lib Magazine, Vol. 13, No. 7/8, July/August 2007 <http://hdl.handle.net/2027.42/58766> |
| Abstract: | The Web puzzle of online information resources often hinders end-users from effective and efficient access to these resources. Clustering resources into appropriate subject-based groupings may help alleviate these difficulties, but will it work with heterogeneous material? The University of Michigan and the University of California Irvine joined forces to test automatically enhancing metadata records using the Topic Modeling algorithm on the varied OAIster corpus. We created labels for the resulting clusters of metadata records, matched the clusters to an in-house classification system, and developed a prototype that would showcase methods for search and retrieval using the enhanced records. Results indicated that while the algorithm was somewhat time-intensive to run and using a local classification scheme had its drawbacks, precise clustering of records was achieved and the prototype interface proved that faceted classification could be powerful in helping end-users find resources. |
| URI: | http://hdl.handle.net/2027.42/58766 |
| ISSN: | 1082-9873 |
| Appears in Collections: | Libraries, University of Michigan
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| 07hagedorn.pdf | | 1596Kb | Adobe PDF | View/Open |
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