II.8 Taxonomy Visualization of Patent Data

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W. Bradford Paley

Elisha F. H. Allgood

Katy Börner

Bruce W. Herr, II

Todd Holloway

Information scientist Katy Börner, graphic designer Elisha F. Hardy, and research programmers Bruce W. Herr II and Todd M. Holloway of the Information Visualization Lab at Indiana University designed and implemented the Taxonomy Visualization (TV) tool in collaboration with interaction designer W. Bradford Paley. The tool supports the semi¬automatic validation and optimization of organization schemas imposed on a data set as a means of structuring and naming. By showing the “goodness of fit” of a schema and the potentially millions of items it organizes, the TV eases the identification and reclassification of misclassified information entities. It also helps with identifying classes that grew over-proportionally, evaluating the size and homogeneity of existing classes, and examining the “well-formedness” of an organization schema. The TV shows the organization schema as a hierarchy in which sublevels are indented according to their depth in the hierarchy. Item properties are represented by bar graphs on the left-hand side of the schema. Item and class interrelations are denoted by line overlays. The map displays the United States Patent and Trademark Office (USPTO) patent classification, which organizes 3 million patents into about 160,000 distinct patent classes. Exemplarily shown are two patents, together with, respectively, their prior art and impact.


References:

Börner, Katy, Elisha F. Hardy, Bruce W. Herr II, Todd Holloway, and W. Bradford Paley. 2007. “Taxonomy Visualization in Support of the Semi-Automatic Validation and Optimization of Organizational Schemas.” Journal of Informetrics 1 (3): 214-225.

Börner, Katy, Elisha F. Hardy, Bruce W. Herr II, Todd M. Holloway, and W. Bradford Paley. 2006. Taxonomy Visualization of Patent Data. Courtesy of Indiana University and W. Bradford Paley. In “2nd Iteration (2006): The Power of Reference Systems,” Places & Spaces: Mapping Science, edited by Katy Börner and Deborah
MacPherson. http://scimaps.org.

Acknowledgements: This exhibit is supported by the National Science Foundation under Grant No. IIS-0238261, CHE-0524661, IIS-0534909 and IIS-0715303, the James S. McDonnell Foundation; Thomson Reuters; the Cyberinfrastructure for Network Science Center, University Information Technology Services, and the School of Library and Information Science, all three at Indiana University. Some of the data used to generate the science maps is from the Web of Science by Thomson Reuters and Scopus by Elsevier. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.