RDF Editing on the Web

ABSTRACT While several tools for simplifying the task of visualizing (SPARQL accessible) RDF data on the Web are available today, there is a lack of corresponding tools for exploiting standard HTML forms directly for RDF editing. The few related existing systems roughly fall in the categories of (a) applications that are not aimed at being reused as components, (b) form generators, which automatically create forms from a given schema -possibly derived from instance data -or (c) form template processors which create forms from a manually created specification. Furthermore, these systems usually come with their own widget library, which can only be extended by wrapping existing widgets. In this paper, we present the AngularJS-based Rdf Edit eXtension (REX) system, which facilitates the enhancement of standard HTML forms as well as many existing AngularJS widgets with RDF editing support by means of a set of HTML attributes. We demonstrate our system though the realization of several usage scenarios

Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project

Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions