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In digital environments such as electronic journals, finding context specific and task related information is a big challenge, simply due to the availability of the huge amounts of data. There are some of techniques to provide the intended information to users of digital communities (e-communities). However, all available techniques have some inherent problems. Thus, users are often frustrated. This book presents new techniques for discovering highly relevant resources. The discovered resources are supplied to users based on the users' local context. For a digital journal, the framework contributes in the following four areas: (i) Finding most related papers from multiple sources, (ii) Discovering and visualizing the relationships between experts, (iii) Linking digital journals with digital libraries maintained by some community leading to serendipitous discoveries of other relevant resources, and (iv) Harvesting pertinent resources from Linked Data. The findings of this research have been implemented for a digital journal which is known as Journal of Universal Computer Science step by step since 2007, and are now either in productive or prototype use.
About the author
Muhammad Tanvir Afzal studied CS at Graz University of Technology, Austria and was awarded Ph.D. with distinction in 2010. He received his master's degree in CS from Quaid-i-Azam University, Islamabad, Pakistan and secured Gold Medal in 2004. His research areas include Digital Libraries, personalized services, Semantic Web,and sentiment Analysis.
