Personalised Access to Linked Data

Recent efforts in the Semantic Web community have been primarily focused on developing technical infrastructure and technologies for efficient Linked Data acquisition, publishing and interlinking. Nevertheless, due to the huge and diverse amount of information, the actual access to a piece of information in the LOD cloud still demands significant amount of effort. In this paper, we present a novel configurable method for personalised access to Linked Data. The method recommends resources of interest from users with similar tastes. To measure the similarity between the users we introduce a novel resource semantic similarity metric, which takes into account the commonalities and informativeness of the resources. We validate and evaluate the method on a real-world dataset from theWeb services domain. The results show that our method outperforms the other baseline methods in terms of accuracy, serendipity and diversity.