Were you too busy during Computers in Libraries 2011 to keep track of all the links flying around on Twitter from the various sessions?
Nobody has time to bookmark every link that appeared in the 9000+ tweets tagged with #CIL11 this week, so I set up a Paper.li edition to comb through the #CIL11 tweets and build a newspaper for each day of the conference so attendees and those following along from home could go back and find resources that were shared at the conference.
The current day’s edition can always found at #CIL11 Conference Daily but you can find each day of the conference in the in the Paper.li archive.
The Library of Congress (LC) is beginning to collect user generated content (UGC) and has begun including blogs in many of its digital collections. Blogs are easy to capture because they have a fairly uniform format and tend to exist for a long time. Continue Reading →
QR Codes are similar to bar codes but they are two-dimensional. They are beginning to appear in many environments, but perhaps the most frequent application is to install a reader on a mobile phone, and then take pictures of the codes, which lead the user to some relevant information. David Lee King described how the Topeka library had a “Big Read QR Scavenger Hunt”, in which people scanned the codes to get clues to the locations on the hunt. The first stop was a bookstore, and the next was an art gallery. Contestants completing all 5 stops on the hunt were entered into a drawing for a basket of rewards.
Even though the hunt was widely advertised, and 300 people started it, only 8 finished. People did not want to drive all over the city to find the codes, and at several of the stores, they were unable to find the posters with the code and were uncomfortable asking about them. But King and the sponsors of the hunt learned a lot about codes in the community, discovered more uses for the technology, and found out that many people are using the technology. So in that respect, the hunt was successful.
John Lutz and Margaret Clark
John Lutz and Margaret Clark from the Florida College of Law Research Center have used QR Codes widely in their library. (Since they furnished a code for their presentation, those with cell phones need read no further!)
Here are some of their applications:
Make content accessible through multiple access points. Students can download PDF of a paper document to their cell phones. (There is a menu option in Adobe Acrobat to put the code on a document.)
Used Microsoft Publisher to create bookmarks with codes to access the catalog, databases, research guides, hours, and their blog.
Put code on every book in new book display that accesses an online review of that book.
Displayed signs on shelves to educate students about online equivalents of print sources.
Printed codes on Avery peelable labels which can be stuck on handouts, etc.
Embedded vCards (a standard for formatting contact information in phones) in codes.
Created videos with Adobe Captivate and uploaded them to YouTube which converts the material to a Flash file and an mpeg file (using the h.264 standard) designed for mobile access.
In one of the best presentations of the conference, Lisa Goddard and Gillian Byrne, librarians at the Memorial University of Newfoundland gave a clear explanation of the technology and issues relating to the semantic web and how it can be applied in libraries.
Why do we need a new Web? We often forget the kids of problems we have with the tools available to us, such as high recall and low precision with Google. The web is very vocabulary dependent. Today’s Web search engines do not group web pages, pull out concepts, or understand them. There is no access to the deep web. Identity is big issue–Google cannot disintermediate between alternate terms, and there is no way to do comparisons. Complex queries are impossible to do on Google. But we do have tools that can handle complex queries such as Scopus. These search engines can do this because they have clearly tagged relational databases on the back end. The semantic web solution is to turn the Web into something like a database, with structured data, controlled vocabularies, and linking. The point is to create machine-actionable data because computers visit websites as often as people do.
The basis of the semantic web is the Resource Description Framework (RDF). RDF objects are described as triples, with a subject, object, and predicate. Here is an example:
Using these triples, we can construct a semantic network of terms:
This allows us to identify related terms, even though the relationship may not be explicitly specified by any of the RDF triples. In the example above, we can see that “Shakespeare” is related to “UK” and “Scotland”. Since every object must have a unique identifier (a URI), its relationship can be resolved.
An ontology is a model describing a particular knowledge domain. Ontologies help establish controlled vocabularies and model relationships between entities and concepts. They have built-in data types that support reasoning because every term must have its own URI. Ontologies are published on the Web and shared. Rules can be written that describe the relationships between terms. For example “wrote” is the inverse of “written by”; “Anne Hathaway married Shakespeare” and “Shakespeare married Anne Hathaway” are symmetrical relationships; and “Shakespeare” and “William Shakespeare” are equivalent. Using these rules, ontologies help computers to reason, and new knowledge can be inferred from given knowledge. Ontologies are written in Web Ontology Language (OWL). The Protege Ontology Library has a list of many of those that are available. Many websites today use RDF,and almost any big technology player has some semantic capabilities.
We talk a lot about search, but not much about data problems, like disconnected data in silos. Many documents are linked with no way to describe the relationships between them, and the links that are used (ISSNs and ISBNs) are not strong identifiers. Since computers cannot see relationships between disparate materials, we need to link concepts, not data. We must think about sharing data in a way that the rest of the world can take advantage of it (and they are not going to adopt MARC!!).
Here are some of the obstacles in implementing the semantic web:
Competing vocabularies: how many ways can you describe a book, article, or place? (See this article for a detailed explanation.)
Co-referencing: different URIs are being created for the same thing. The sameAs tool developed at the University of Southampton helps find existing URIs and prevent this problem.
There is lots and lots of data out there; how can we find it? The CKAN Data Hub is a helpful registry system for linked data, but people still must submit their data.
Linked data sets are being released without good examples or good ways to search the data.
We are good at sharing but not so good at trusting. We need the trust to link. External taxonomies are now beginning to be trusted, but work on the attribution of data sets is needed.
Preservation. What happens when an ontology or linking hub disappears? Chaos could result.
Ownership: Who owns the data? In an academic environment, we do not own the data; the vendors do.
Licensing: There is no correlation between open and linked data. VoID (Vocabulary of Interlinked Datasets) is a schema to describe linked datasets and allows you to say that your dataset has a license.
All of these issues involved hard work on top of what librarians do now. We are in an age of chaotic innovation in libraries. Fortunately, there are some “chaos tamers” available to help us.
