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The Semantic Data are built from triples, that contain subjects, predicates and objects. On the other hand we can consider the triples as edges. The subject and the object are the nodes and the predicate is the label of the edge. In this view the Semantic Data define a graph. This graph can be very large, because a Semantic Dataset contains millions of triples. To query this dataset we can use the...
Recent years recommender systems have been used in the public cultural services to provide personalized services. The single recommendation methods only cover the limited areas of interest of users. To address this issue, this paper proposes an integrated recommender system for cross-domain heterogeneous recommendations of the public cultural resources. The proposed system works on the recommendations...
This paper proposes a novel approach to book recommendation: we utilise big data created by thousands of book social cataloguing website users and treat it as a collectively written meta-annotation of a book. After learning semantic similarity between a large collection of books by applying algorithms of natural language processing (probabilistic topic models and semantic neural networks) to the symbolic...
People develop personal information collections consisting of distributed web resources as both reminders that resources exist and to provide rapid access to these resources. Managing such collections is necessary to preserve their value. Unexpected changes within distributed collections can cause them to become outdated, requiring revisions to or removal of no-longer-appropriate resources and replacements...
Convolutional Neural Networks (CNNs) have substantially advanced the state-of-the-art accuracies of object recognition, which is the core function of a myriad of modern multimedia processing techniques such as image/video processing, speech recognition, and natural language processing. GPU-based accelerators gained increasing attention because a large amount of highly parallel neurons in CNN naturally...
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