Linking electronic journals - Lessons from the Open Journal project

The Open Journal project has completed its three year period of funding by the UK Electronic Libraries (eLib) programme (Rusbridge 1998). During that time, the number of journals that are available electronically leapt from a few tens to a few thousand. Some of these journals are now developing the sort of features the project has been advocating, in particular the use of links within journals, between different primary journals, with secondary journals data, and to non-journal sources. Assessing the achievements of the project and considering some of the difficulties it faced, we report on the different approaches to linking that the project developed, and summarise the important user responses that indicate what works and what does not. Looking ahead, there are signs of change, not just to simple linking within journals but to schemes in which links are the basis of "distributed" journals, where information may be shared and documents built from different sources. The significance has yet to be appreciated, but this would be a major change from printed journals. If projects such as this and others have provided the initial impetus, the motivation for distributed journals comes, perhaps surprisingly, from within certain parts of the industry, as the paper shows.

'An Artificial Immune System as a Recommender System for Web Sites'

Artificial Immune Systems have been used successfully to build recommender systems for film databases. In this research, an attempt is made to extend this idea to web site recommendation. A collection of more than 1000 individuals' web profiles (alternatively called preferences / favourites / bookmarks file) will be used. URLs will be classified using the DMOZ (Directory Mozilla) database of the Open Directory Project as our ontology. This will then be used as the data for the Artificial Immune Systems rather than the actual addresses. The first attempt will involve using a simple classification code number coupled with the number of pages within that classification code. However, this implementation does not make use of the hierarchical tree-like structure of DMOZ. Consideration will then be given to the construction of a similarity measure for web profiles that makes use of this hierarchical information to build a better-informed Artificial Immune System.

'An Artificial Immune System as a Recommender System for Web Sites'

Artificial Immune Systems have been used successfully to build recommender systems for film databases. In this research, an attempt is made to extend this idea to web site recommendation. A collection of more than 1000 individuals' web profiles (alternatively called preferences / favourites / bookmarks file) will be used. URLs will be classified using the DMOZ (Directory Mozilla) database of the Open Directory Project as our ontology. This will then be used as the data for the Artificial Immune Systems rather than the actual addresses. The first attempt will involve using a simple classification code number coupled with the number of pages within that classification code. However, this implementation does not make use of the hierarchical tree-like structure of DMOZ. Consideration will then be given to the construction of a similarity measure for web profiles that makes use of this hierarchical information to build a better-informed Artificial Immune System.