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Entity linking is one of the problems to be handled in order to process natural language and to enrich the existing unstructured text with metadata. The generation of assignments between knowledge base entities and lexical units is called entity linking. Although a number of systems have been proposed for linking entity mentions in various languages, there is currently no publicly available entity linking system specific to the Turkish language. This paper presents a novel entity linking system—THINKER - for linking Turkish content with entities defined in the Turkish dictionary (tdk.gov.tr) or Turkish Wikipedia (tr.wikipedia.org). Specifically, we first propose a novel machine learning based entity detection algorithm for the Turkish language. Then, we propose a collective disambiguation algorithm which utilizes a set of metrics for the linking task and, which is optimized using a genetic algorithm. The effectiveness of THINKER is validated empirically over generated data sets. The experimental results show that THINKER outperformed the state-of-the-art cross-lingual and multilingual entity linking systems in the literature. High entity linking performance (74.81 percent F1 score) is achieved by extending previous methods with some features specific to Turkish language and by developing a novel method that can learn better representations of entity embeddings. [ABSTRACT FROM PUBLISHER]

Based on compelling evidence from responses to an April 2013 call for proposals (CFP) on energy-efficient video consumption, MPEG initiated standardization of Green Metadata for energy-efficient video consumption. This article describes how metadata enables large power reductions when QoE is maintained and even larger reductions when QoE is allowed to vary. When QoE is maintained, metadata enables average power reductions of 12, 12, and 26 percent during encoding, decoding, and display, respectively. In addition, the authors measured up to 80 percent power savings at lowered, but acceptable, QoE levels. This article describes the functional architecture of a system that exploits the Green Metadata standard for energy-efficient media consumption, referring to this functional architecture to explain power reductions in various system components. [ABSTRACT FROM PUBLISHER]

Visualization of geospatial data provenance aims to provide a user-friendly way for easy navigation and increased understanding of the derivation history of geoscientific results. Most existing work focuses on provenance modeling and management. This paper proposes to interleave map and faceted metadata for geospatial data provenance visualization and navigation. It shows how provenance in Web-geoprocessing workflows can be visualized at four levels: feature, dataset, service, and knowledge. A prototypical system, named GeoPWProv, is developed to demonstrate the applicability of the approach. [ABSTRACT FROM AUTHOR]