Volume 12 (2012): Issue 4 (December 2012)

The paper describes a semantic technology based environment intended for developing technology enhanced learning applications in humanitarian problem domains. The environment consists of three layers: the storage layer contains heterogeneous repositories storing domain and pedagogical knowledge; the tool level contains a set of tools for processing different types of knowledge and the middleware layer is implemented as an extended search engine carrying out all necessary communications between the tools and the repositories. Some implementation issues are discussed and a preliminary evaluation of the environment based on an exploitation is presented.

The paper presents an approach to active learning facilitated by the use of semantic technologies. Some features of active learning and understanding of learning in humanities are discussed. The specifics of a well defined learning task - learner’s authoring of analytical materials, grounded by materials from a digital library - are analyzed to shape the functionality of experimental Technology Enhanced Learning (TEL) environment with a built-in domain and pedagogical knowledge. The environment structure and realization are discussed and a learning example is presented.

The paper presents new applications of the Ontology-to-Text Relation Strategy to Bulgarian Iconographic Domain. First the strategy itself is discussed within the triple ontology-terminological lexicon-annotation grammars, then - the related works. Also, the specifics of the semantic annotation and evaluation over iconographic data are presented. A family of domain ontologies over the iconographic domain are created and used. The evaluation against a gold standard shows that this strategy is good enough for more precise, but shallow results, and can be supported further by deep parsing techniques.

This paper presents an approach for Information Extraction (IE) from Patient Records (PRs) in Bulgarian. The specific terminology and lack of resources in electronic format are some of the obstacles that make the task of current patient status data extraction in a structured format quite challenging. The usage of N-grams, collocations and words’ distances allows us to cope with this problem and to extract automatically the attribute-value pairs with relatively high precision.

The paper presents an overview of the image-processing techniques. The set of basic theoretical instruments includes methods of mathematical analysis, linear algebra, probability theory and mathematical statistics, theory of digital processing of one-dimensional and multidimensional signals, wavelet-transforms and theory of information. This paper describes a methodology that aims to detect and diagnose faults, using thermographs approaches for the digital image processing technique.

Support Vector Machines (SVMs) have gained prominence because of their high generalization ability for a wide range of applications. However, the size of the training data that it requires to achieve a commendable performance becomes extremely large with increasing dimensionality using RBF and polynomial kernels. Synthesizing new training patterns curbs this effect. In this paper, we propose a novel multiple kernel learning approach to generate a synthetic training set which is larger than the original training set. This method is evaluated on seven of the benchmark datasets and experimental studies showed that SVM classifier trained with synthetic patterns has demonstrated superior performance over the traditional SVM classifier.