Tagging Museum Collections

Steve, an open source social tagging project focused on museum collections, is exploring how folksonomies might improve access to art and encourage “engagement with cultural content.” The project invites interested users to generate descriptions of works of art using their own terms (via their Web site) as a way of supplementing the highly-structured vocabulary systems created by museum professionals. This user-generated cataloging might provide the museums with the “missing subject-based information for their collections databases” required to improve their e-resources and finding aids. Steve collaborators include the Denver Art Museum, Guggenheim Museum, Cleveland Museum of Art, Indianapolis Museum of Art, Los Angeles County Museum of Art, Metropolitan Museum of Art, Minneapolis Institute of Arts, Rubin Museum of Art, and the San Francisco Museum of Modern Art.

A screenshot of the user tagging tool in Steve.

Steve holds great promise for expanding access to museum collections Online, such as in the area of art education for primary and secondary students. Using a folksonomy can be a highly-effective means of capturing the “visual language” of younger students. It can aid in the discovery of works of art for school projects, allow students to compare how different artists and movements represented similar objects, and provide them with a means of expressing concepts of aesthetic response and analysis using their own words. Tagging art can also help students begin to understand and question the categories and labels that have been applied to art collections, schools, and individual artists by scholars and curators over time.

For more advanced researchers, Steve provides “a testbed for hypotheses about the social experiences offered by museums to both on-site and on-line visitors” and the ability to study the difference between “expert and non-professional vocabularies.” Researchers interested in sharing thoughts about social tagging art and related topics can participate in the Steve discussion lists. The project also plans to license data sets for analysis and to share its research findings among peer communities and institutions.

Projects links:
Steve homepage
Steve registration page (for those interested in tagging)
Steve for developers (download code)
Papers, presentations, background docs., etc.
Discussion list

Philology 2.0

Imaging the following scenario:

Background information and the text [of a Greek author like Homer] [...] are translated into the Chinese or Arabic. The inquirer has developed a profile, not unlike her medical history, which can record the classes she has taken, the books she has read, the movies she has seen, the games she has played, and the questions that she has posed. The personal reading agent can compare this profile, eagerly developed and shared only in part and under strict conditions, against the cultural referents implicit in the author or text of interest, then produce not only translations but personalized briefing materials – maps, timelines, diagrams, simulations, glossary entries – to help that reader contextualize what she has encountered. As the reader begins to ask questions, the system refines its initial hypotheses, quickly adapting itself to her needs. As the system changes, it inspires new kinds of inquiry in the reader, creating a feedback loop that encourages their conversation to evolve.

A compelling essay by Gregory Crane, David Bamman and Alison Babeu of the Perseus Digital Library project at Tufts University entitled, ePhilology: when the books talk to their readers (.pdf), uses the above scenario to illustrate the “optimal digital future” of philological research. The article, part of the forthcoming Blackwell Companion to Digital Literary Studies (Ray Siemens and Susan Schreibman eds., 2007), explores topics such as: text mining, “smart books” (documents that learn from each other), texts that adapt themselves to their users, and tools that help scholars identify trends in the secondary literature.

An example of personalization from the Perseus Digital Library: “Once a user has asked for information on four or five words in a three hundred word passage of Ovid, we can then predict two thirds of the subsequent words that will elicit queries. This recommender system is similar in principle to the systems which Amazon and other e-commerce systems use to show consumers new products based on the products purchased by people who also bought product X. The application, however, reduces the search space of a language passage, suggesting words for study rather than products for purchase.”

The authors identify six features that distinguish emerging digital resources:

1. they can be delivered to any point on the earth and at any time

2. they can be fundamentally hypertextual, supporting comprehensive links between assertions and their evidence

3. they dynamically recombine small, well defined units of information to serve particular people at particular times

4. they learn on their own and apply as many automated processes as possible, not only automatic indexing but morphological and syntactic analysis, named entity recognition, knowledge extraction, machine translation etc., with changes in automatically generated results tracked over time

5. they learn from their human readers and can make effective use of contributions, explicit and implicit, from a range of users in real time

6. they automatically adapt themselves to the general background and current purposes of their users.

Beyond a discussion of the latest digital technologies for textual analysis, the essay also contemplates larger issues related to ePhilology: the creation of new “spaces” that will advance the study of literature; systems that will inspire new forms of inquiry for readers; and unexpected discoveries that will arise from repurposing, sharing, and enriching the great texts of the ancients.