The Semantic Web Research has resulted in the last years in significant outcomes. Various industries have adopted semantic web technologies, while the "deep web" is still pursuing the critical transformation point, in which the majority of data found on deep web will be exploited through semantic web value layers. In this article we analyze the Semantic Web applications from a "market" perspective. We are setting the key requirements for Real World information systems semantic web enabled and we discuss the major difficulties for the semantic web uptake that has been delayed. This article contributes to the literature of semantic web and knowledge management providing a context for discourse towards best practices on semantic web based information systems.

We investigate with Raman spectroscopy how gold nanostructures of different shape, size and geometry locally modify a graphene cover layer through strain. The resulting phonon softening translates into frequency downshifts of up to 85 cm–1 for the 2D‐mode of graphene. With spatially resolved and excitation dependent Raman measurements we demonstrate that the downshifted Raman peaks exclusively arise from strained graphene subject to plasmonic enhancement by the nanostructures. The signals arise from an area well below the size of the laser spot. They serve as a local probe for the interaction between graphene and intense light fields. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Background: In the field of protein engineering and biotechnology, the discovery and characterization of structural patterns is highly relevant as these patterns can give fundamental insights into protein-ligand interaction and protein function. This paper presents GSP4PDB, a bioinformatics web tool that enables the user to visualize, search and explore protein-ligand structural patterns within the entire Protein Data Bank. Results: We introduce the notion of graph-based structural pattern (GSP) as an abstract model for representing protein-ligand interactions. A GSP is a graph where the nodes represent entities of the protein-ligand complex (amino acids and ligands) and the edges represent structural relationships (e.g. distances ligand -amino acid). The novel feature of GSP4PDB is a simple and intuitive graphical interface where the user can "draw" a GSP and execute its search in a relational database containing the structural data of each PDB entry. The results of the search are displayed using the same graph-based representation of the pattern. The user can further explore and analyse the results using a wide range of filters, or download their related information for external post-processing and analysis. Conclusions: GSP4PDB is a user-friendly and efficient application to search and discover new patterns of protein-ligand interaction.

Most approaches to B2B interoperability are based on language syntax standardisation, usually by XML Schemas. However, due to XML expressivity limitations, they are difficult to put into practice because language semantics are not available for computerised means. Therefore, there are many attempts to use formal semantics for B2B based on ontologies. However, this is a difficult jump as there is already a huge XML-based B2B framework and ontology-based approaches lack momentum. Our approach to solve this impasse is based on a direct and transparent transfer of existing XML Schemas and XML data to the semantic world. This process is based on a XML Schema to web ontology mapping combined with an XML data to semantic web data one. Once in the semantic space, it is easier to integrate different business standards using ontology alignment tools and to develop business information systems thanks to semantics-aware tools.

Abstract. The development of information and communication technologies has stimulated a variety of data and informational resources about human behavior. This is contributing toward collaborative efforts in the formalization and systematization of an overwhelming volume of scientific information. Several tools are helpful for this endeavor, among which the ontology is growing in popularity. Most of the available informational resources adopt the ontology to organize a shared conceptualization of a given body of knowledge. In the present study, we reviewed ontology resources (n = 17) that can be of interest to researchers and scholars involved in human behavior and psychological research. The selected ontologies were contrasted on the three main components of ontologies, classes, individuals, and properties, and on scheme and knowledge metrics. Moreover, we recorded the associations of the terms within a given ontology with terms of other ontologies (mappings), the number of projects using a particular ontology, and whether an ontology was available within the Bioportal, an extensive repository about biomedical ontologies. A few working examples were also provided to clarify how these resources might contribute to improve the analysis, understanding, and research cooperation about human behavior and psychological research.

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Awareness is required for supporting all forms of cooperation. In Computer Supported Collaborative Learning (CSCL), awareness can be used for enhancing collaborative opportunities across physical distances and in computer-mediated environments. Shared Knowledge Awareness (SKA) intends to increase the perception about the shared knowledge students have in a collaborative learning scenario and also concerns the understanding that this group has about it. However, it is very difficult to produce accurate awareness indicators based on informal message exchange among participants. Therefore, we propose a semantic system for cooperation that makes use of formal methods for knowledge representation based on Semantic Web technologies. From these semantics-enhanced repository and messages, it could be easier to compute more accurate awareness.

One of the main problems when developing graph-based applications is the availability of large and representative datasets. The lack of real graphs has motivated the development of software tools for generating synthetic graphs. R-MAT is a data generation method that was designed to produce synthetic graphs whose characteristics resemble those occurring in real networks. Although the generation model defined by R-MAT is easy to understand, its implementation is not trivial and it has intrinsic memory restrictions that makes the generation of very large graphs difficult. This paper studies the practical implementation of R-MAT. We discuss the issues of the original implementation which works with the adjacency matrix of the graph and analyze the size of the resulting graph obtained with the R-MAT model. Then, we introduce and experimentally evaluate R 3 MAT, an alternative implementation for R-MAT based on an array of degrees. These experiments show that (i) our R 3 MAT is able to generate graphs of hundred million nodes and billion edges in a single machine, (ii) our method preserves the characteristic power-law distribution of the edge degrees present in real-world graphs, and (iii) R 3 MAT has the best performance in the current state of the art, when considering a single modest computer in a sequential fashion.