Social Bookmarking services have spread over the last few years. People often use tagging to organize and share their bookmarks. But this process can also be overloading the users and recommender systems are a popular approach to address this issue. Our goal was to explore the potential of services for semantic annotation and entity extraction to assist tagging and generate recommendations in social bookmarking communities. For that purpose, we have built a prototype of a Linked Data-based recommender and social bookmarking system. The system uses Zemanta to generate semantic tags and later the tags are the basis upon which recommendations are calculated. After that, a set of people used the application, gave feedback and evaluated the recommendations the system generated. In addition, we give a proposal of how tag connections to Linked Data entities expressed with MOAT ontology represent boost reusability and interoperability of gathered information
Social bookmarking, semantic web, semantic annotation, web services, data, information, sharing, entity extraction, Linked Data, collaboration
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