April 13-17, 2010
Denver, Colorado, USA

Sessions: Abstract

Can Structured Metadata Play Nice with Tagging Systems? Parsing New Meanings From Classification-Based Descriptions on Flickr Commons   go to paper

Joe Dalton, The New York Public Library, USA

By the time The New York Public Library (NYPL) joined The Commons on Flickr, almost a year after The Library of Congress (LOC) had launched the initial pilot project, it was clear there is great potential for user-generated content to shed new light on archival imagery. in ways that are difficult to achieve with more traditional methods. Many of the earliest Commons images contained little or no prior description, and users were encouraged to tag these records with much-needed metadata. Images uploaded more recently by Commons partners often have included associated metadata, and this fact has been dealt with differently by various institutions. Some choose to not upload that data; others upload subject-headings, but only as descriptive text; still others add selected subject headings as single tags across a set of items.

When the library uploaded its first set of 1,300 images in late 2008, it was thus faced with a number of questions about what type of metadata should also be uploaded. Should we hide or cloak the structured metadata (subject headings, name authority files, etc.) associated with these images? Or could we try to contribute our pre-existing subjects as tags? Although Flickr machine-tags have emerged as one option for exposing controlled vocabularies on Flickr, what if our structured metadata could look – and behave – more like user-generated tags from the beginning?

This paper discusses the rationale behind NYPL's decision to combine existing metadata – in the form of subject headings – with user-generated tags, and demonstrates some of the challenges, benefits and drawbacks for institutions that may be interested in using similar approaches for their own collections.

Session: Collections: Tag / Search / Deploy - Part 1 [access]

Keywords: tagging, folksonomy, metadata, Flickr Commons, classification systems