Contributed by Organizations or Campuses; Articles, Papers, and Reports; Information Discovery and Retrieval; and Search Engines

The History Engine site is an online resource for students to search and browse written tidbits — what historians behind the project call “episodes” — and to contribute their own entries to a growing online ecosystem that is connected via semantic links, time stamps and geographic tags (with mapping functionality on the way).

A tool from MIT finds keywords so that students can efficiently review lectures.

The Google Books Project has drawn a great deal of attention, offering the prospect of the library of the future and rendering many other library and digitizing projects apparently superfluous. To grasp the value of Google’s endeavor, we need among other things, to assess its quality. On such a vast and undocumented project, the task is challenging. In this essay, I attempt an initial assessment in two steps. First, I argue that most quality assurance on the Web is provided either through innovation or through “inheritance.” In the later case, Web sites rely heavily on institutional authority and quality assurance techniques that antedate the Web, assuming that they will carry across unproblematically into the digital world. I suggest that quality assurance in the Google’s Book Search and Google Books Library Project primarily comes through inheritance, drawing on the reputation of the libraries, and before them publishers involved. Then I chose one book to sample the Google’s Project, Lawrence Sterne’s Tristram Shandy. This book proved a difficult challenge for Project Gutenberg, but more surprisingly, it evidently challenged Google’s approach, suggesting that quality is not automatically inherited. In conclusion, I suggest that a strain of romanticism may limit Google’s ability to deal with that very awkward object, the book.

An eye tracking experiment revealed that college student users have substantial trust in Google's ability to rank results by their true relevance to the query. When the participants selected a link to follow from Google's result pages, their decisions were strongly biased towards links higher in position even if the abstracts themselves were less relevant. While the participants reacted to artificially reduced retrieval quality by greater scrutiny, they failed to achieve the same success rate. This demonstrated trust in Google has implications for the search engine's tremendous potential influence on culture, society, and user traffic on the Web.

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.