in annotation graph datasets. In this paper, we propose a novel approach for link prediction; it is a preliminary task when discovering more complex patterns. Our prediction is based on a complementary methodology of graph summarization (GS) and dense subgraphs (DSG). GS can exploit and summarize knowledge

mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical

identiªable as the P3a potential. Based on prior research linking P3a with novelty detection and with the frontal lobes, we predicted that P3a would be reduced to the extent that novelty detection and memory retrieval interfere with each other. Results during episodic and semantic retrieval tasks were

The advent of the Linked Open Data (LOD) initiative gave birth to a variety of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper we present SPrank, a novel hybrid

and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method

We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch

The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to graph data, where nodes represent entities, edges relationships between entities, and feature vectors associated

. The tutorial will show how Linked Data enables uniform access, parsing and interpretation of data, and how this novel wealth of structured data can potentially be exploited for creating new applications or enhancing existing ones. As such, the tutorial will focus on Linked Data publishing and related Semantic

advertising can be more effective and the recom-mendations for users will also be improved. In this paper, we propose to predict the link relations between users and shops based on the following behavior. In order to better model the characteristics of the shops, we first adopt Topic Modeling to analyze

-factored features of the syntactic paths behind semantic roles. Overall, our method for SRL is a novel way to ex-ploit larger variability in the syntactic re-alizations of predicate-argument relations, moving away from pipeline architectures. Experiments show that our approach im-proves the robustness