Friday, May 09, 2008

Metadata for Learning Resources

Metadata for Learning Resources: An Update on Standards Activity for 2008 by Sarah Currier appears in the latest issue of Ariadne.
The major areas of development covered in this article are:
  1. LOM Next: plans for the next version of the IEEE LOM
  2. The Joint DCMI/IEEE LTSC (Learning Technology Standards Committee) Taskforce: bringing together the two major metadata standards used for learning resources, and providing an RDF translation for the LOM
  3. DC-Education Application Profile (DC-Ed AP): a modular application profile purely looking at educational aspects of resources, based on community requirements
  4. The United Kingdom’s Joint Information Systems Committee Learning Materials Application Profile (JISC LMAP) scoping study: working alongside a number of similar projects looking at application profiles for repositories in other areas, e.g. images.
  5. International Standards Organisation Metadata for Learning Resources (ISO MLR): based primarily in Canada, this international standards body is devising a new international standard for educational metadata, in response to perceived limitations of the IEEE LOM
  6. The European Commission’s PROLEARN Harmonisation of Metadata project: a study into the issues and challenges of achieving harmonisation in metadata, given the heterogeneous landscape

Thursday, May 08, 2008

Metadata Advocates

I had an Ah-Ha moment while listening to John Udell's show Interviews with Innovators. The episode was Working with Data Sources with Raymond Yee.
Raymond Yee is a lecturer at the UC Berkeley School of Information and the author of Pro Web 2.0 Mashups: Remixing Data and Web Services. In this conversation he talks about teaching students how to work with existing data sources, and speculates with Jon Udell on ways to expand the supply of available sources.
What struck me was that we should be advocates for metadata standards. If the local geneology society puts up a calendar on their website, help them get it into iCal or hCal format. Then we could drop their info into a pathfinder. Or geocoding the local bird-watchers sightings, or school district's lunch menu, or .... We could offer our understanding of the importance of standards and data reuse to our community. The library benefits by becoming the go-to-place for information management. The community benefits because they get the word out more effectively. It would be a very different job description for a cataloger to become the community data standard outreach person. But, not a bad place to be.

Resource Description and Access

Now available, Outcomes of the Meeting of the Joint Steering Committee Held in Chicago, USA, 13-22 April 2008.

Wednesday, May 07, 2008

Using Wikipedia

Two new reports from HP Labs show interesting uses of Wikipedia in information management.

Boosting Inductive Transfer for Text Classification using Wikipedia by Somnath Banerjee. HPL-2008-42
Inductive transfer is applying knowledge learned on one set of tasks to improve the performance of learning a new task. Inductive transfer is being applied in improving the generalization performance on a classification task using the models learned on some related tasks. In this paper, we show a method of making inductive transfer for text classification more effective using Wikipedia. We map the text documents of the different tasks to a feature space created using Wikipedia, thereby providing some background knowledge of the contents of the documents. It has been observed here that when the classifiers are built using the features generated from Wikipedia they become more effective in transferring knowledge. An evaluation on the daily classification task on the Reuters RCV1 corpus shows that our method can significantly improve the performance of inductive transfer. Our method was also able to successfully overcome a major obstacle observed in a recent work on a similar setting. Publication Info: Published and presented at ICMLA 2007, the Sixth International Conference on Machine Learning and Applications (ICMLA'07), 13-15 Dec. 2007 Cincinnati, Ohio, USA
Clustering Short Texts using Wikipedia by Somnath Banerjee, Krishnan Ramanathan, and Ajay Gupta. HPL-2008-41
Subscribers to the popular news or blog feeds (RSS/Atom) often face the problem of information overload as these feed sources usually deliver large number of items periodically. One solution to this problem could be clustering similar items in the feed reader to make the information more manageable for a user. Clustering items at the feed reader end is a challenging task as usually only a small part of the actual article is received through the feed. In this paper, we propose a method of improving the accuracy of clustering short texts by enriching their representation with additional features from Wikipedia. Empirical results indicate that this enriched representation of text items can substantially improve the clustering accuracy when compared to the conventional bag of words representation. Publication Info: Published and presented at SIGIR 2007, the 30th Annual International ACM SIGIR Conference, 23-27 July 2007, Amsterdam, Netherlands

Monday, May 05, 2008

Slick Deal

Here is a bargain offered by Amazon, OCLC - MARC Record. It has free shipping too! This was seen on Slick Deals.

Don't they know they can get all the free MARC records they want from their local library?

Thanks Walter.