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Author(s):  
Adeyinka Tella ◽  
Victoria Okojie ◽  
O. T. Olaniyi

Digital libraries use the semantic web and social networking technologies to improve browsing and searching for resources. With digital libraries' social and semantic services, every library user has the opportunity to bookmark interesting books, articles, or other materials in semantically annotated directories. Social bookmarking is indispensable to digital libraries. This chapter discusses some of the popular social bookmarks adopted in the digital libraries, the important requirements for including social bookmarking in a digital library system, the design principles of social bookmarks, features of social bookmarking tools, digital libraries and links with social bookmarking, social tagging, social bookmark and digital libraries, advantages and disadvantages of social tagging in digital libraries. The chapter highlights tips that users need to consider when using social bookmarking in digital libraries. The authors conclude that projecting into the future, it is expected that, more digital libraries will incorporate social bookmarking to enhance collaboration among their users.


2013 ◽  
Vol 96 (3) ◽  
pp. 24-30 ◽  
Author(s):  
Hiroshi Yakushigawa ◽  
Hidekazu Yanagimoto ◽  
Michifumi Yoshioka

2012 ◽  
Vol 132 (2) ◽  
pp. 313-318
Author(s):  
Hiroshi Yakushigawa ◽  
Hidekazu Yanagimoto ◽  
Michifumi Yoshioka

Author(s):  
Takehiro Yamaguchi ◽  
◽  
Ayahiko Niimi ◽  

In this study, we treat transactional sets of data streams as a graph sequence. This graph sequence represents both the relational structures of data for each period and changes in these structures. In addition, we analyze changes in a community in this graph sequence. Our proposed algorithm extracts community transition rules to detect communities that appear irregularly in a graph sequence using our proposed method combined with adaptive graph kernels and hierarchical clustering. In experiments using synthetic datasets and social bookmark datasets, we demonstrate that our proposed algorithm detects changes in a community appearing irregularly.


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