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Abstract-As the number of Web 2.0 sites offering tagging facilities for the users' voluntary content annotation increases, so do the efforts to analyze social phenomena resulting from generated tagging and folksonomies. Most of these efforts provide different views for the understanding of various web activities.Results from various experimental research should be utilized to improve existing approaches underlying tagging data and contribute further to weaving the Web. However, in practice, there are not enough solutions taking advantage of these results. Even though we can mine social relations via tagging data, it proves no worth for users if this data cannot be reused.In this paper we propose a solution for tag data representation which allows data reuse across different tagging systems. To achieve this goal, we analyze current social tagging practices, existing folksonomy usage as well as Semantic Web approaches to data annotation and tagging. We survey and compare existing tag ontologies in an attempt to investigate mapping possibilities between different conceptual models. Finally, we present our method for federation among existing ontologies in order to generate re-usable, semantically-linked data that will underly tagging data.

Existent rigid unit mode (RUM) models based on rotating squares, which may explain the phenomenon of negative thermal expansion (NTE), are generalized so as to assess the NTE potential for novel systems made from rectangular or rhombic rigid units. Analytical models for the area coefficients of thermal expansion (CTE) of these innovative networks are derived in an attempt to determine the optimal geometrical parameters and connectivity for maximum NTE. It was found that all systems exhibit NTE, the extent of which is determined by the shape and connectivity of the elemental rigid units (side lengths ratio or internal angle). It was also found that some of the networks proposed here should exhibit significantly superior NTE properties when compared with the well-known network of squares, and that for optimal NTE characteristics, pencil-like rigid units should be used rather than square-shaped ones, as these permit larger pore sizes that are more conducive to NTE. All this compliments earlier work on the negative Poisson's ratio (auxetic) potential of such systems and may provide a route for the design of new materials exhibiting superior thermo-mechanical characteristics including specifically tailored CTEs or giant NTE characteristics.

A worldwide movement towards the publication of Open Government Data is taking place, and budget data is one of the key elements pushing this trend. Its importance is mostly related to transparency, but publishing budget data, combined with other actions, can also improve democratic participation, allow comparative analysis of governments and boost data-driven business. However, the lack of standards and common evaluation criteria still hinders the development of appropriate tools and the materialization of the appointed benefits. In this paper, we present a model to analyse government initiatives to publish budget data. We identify the main features of these initiatives with a double objective: (i) to drive a structured analysis, relating some dimensions to their possible impacts, and (ii) to derive characterization attributes to compare initiatives based on each dimension. We define use perspectives and analyse some initiatives using this model. We conclude that, in order to favour use perspectives, special attention must be given to user feedback, semantics standards and linking possibilities.

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Many LOD datasets, such as DBpedia and LinkedGeoData, are voluminous and process large amounts of requests from diverse applications. Many data products and services rely on full or partial local LOD replications to ensure faster querying and processing. While such replicas enhance the flexibility of information sharing and integration infrastructures, they also introduce data duplication with all the associated undesirable consequences. Given the evolving nature of the original and authoritative datasets, to ensure consistent and up-to-date replicas frequent replacements are required at a great cost. In this paper, we introduce an approach for interest-based RDF update propagation, which propagates only interesting parts of updates from the source to the target dataset. Effectively, this enables remote applications to 'subscribe' to relevant datasets and consistently reflect the necessary changes locally without the need to frequently replace the entire dataset (or a relevant subset). Our approach is based on a formal definition for graphpattern-based interest expressions that is used to filter interesting parts of updates from the source. We implement the approach in the iRap framework and perform a comprehensive evaluation based on DBpedia Live updates, to confirm the validity and value of our approach.

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The Web of Data is an increasingly rich source of information, which makes it useful for Big Data analysis. However, there is no guarantee that this Web of Data will provide the consumer with truthful and valuable information. Most research has focused on Big Data's Volume, Velocity, and Variety dimensions. Unfortunately, Veracity and Value, often regarded as the fourth and fifth dimensions, have been largely overlooked. In this paper we discuss the potential of Linked Data methods to tackle all five V's, and particularly propose methods for addressing the last two dimensions. We draw parallels between Linked and Big Data methods, and propose the application of existing methods to improve and maintain quality and address Big Data's veracity challenge