Catálogo de publicaciones - libros

One of the crucial tasks towards the realization of the Semantic Web vision is the efficient encoding of human knowledge in ontologies. The proper maintenance of these, usually large, structures and, in particular, their adaptation to new knowledge (ontology evolution) is one of the most challenging problems in current Semantic Web research. In this paper, we uncover a certain gap in current ontology evolution approaches and propose a novel research path based on belief change. We present some ideas in this direction and argue that our approach introduces an interesting new dimension to the problem that is likely to find important applications in the future.

In this paper, we propose a novel holistic methodology for keyword search in historical typewritten documents combining synthetic data and user’s feedback. The holistic approach treats the word as a single entity and entails the recognition of the whole word rather than of individual characters. Our aim is to search for keywords typed by the user in a large collection of digitized typewritten historical documents. The proposed method is based on: (i) creation of synthetic image words; (ii) word segmentation using dynamic parameters; (iii) efficient hybrid feature extraction for each image word and (iv) a retrieval procedure that is optimized by user’s feedback. Experimental results prove the efficiency of the proposed approach.

Image fingerprinting systems aim to extract unique and robust image descriptors (in analogy to human fingerprints). They search for images that are not only perceptually similar but replicas of an image generated through mild image processing operations. In this paper, we examine the use of color descriptors based on a 24-color quantized palette for image fingerprinting. Comparisons are provided between different similarity measures methods as well as regarding the use of color-only and spatial chromatic histograms.

In this paper a method for feature selection and classification of email spam messages is presented. The selection of features is performed in two steps: The selection is performed by measuring their entropy and a fine-tuning selection is implemented using a genetic algorithm. In the classification process, a Radial Basis Function Network is used to ensure robust classification rate even in case of complex cluster structure. The proposed method shows that, when using a two-level feature selection, a better accuracy is achieved than using one-stage selection. Also, the use of a lemmatizer or a stop-word list gives minimal classification improvement. The proposed method achieves 96-97% average accuracy when using only 20 features out of 15000.

This work studies the problem of violence detection in audio data, which can be used for automated content rating. We employ some popular frame-level audio features both from the time and frequency domain. Afterwards, several statistics of the calculated feature sequences are fed as input to a Support Vector Machine classifier, which decides about the segment content with respect to violence. The presented experimental results verify the validity of the approach and exhibit a better performance than the other known approaches.

This paper addresses the problem of text classification in high dimensionality spaces by applying linear weight updating classifiers that have been highly studied in the domain of machine learning. Our experimental results are based on the Winnow family of algorithms that are simple to implement and efficient in terms of computation time and storage requirements. We applied an exponential multiplication function to weight updates and we experimentally calculated the optimal values of the learning rate and the separating surface parameters. Our results are at the level of the best results that were reported on the family of linear algorithms and perform nearly as well as the top performing methodologies in the literature.

We use decision trees and genetic algorithms to analyze the academic performance of students throughout an academic year at a distance learning university. Based on the accuracy of the generated rules, and on cross-examinations of various groups of the same student population, we surprisingly observe that students’ performance is clustered around tutors.

Traditional document clustering techniques rely on single-term analysis, such as the widely used Vector Space Model. However, recent approaches have emerged that are based on Graph Models and provide a more detailed description of document properties. In this work we present a novel Significance-based Graph Model for Web documents that introduces a sophisticated graph weighting method, based on significance evaluation of graph elements. We also define an associated similarity measure based on the maximum common subgraph between the graphs of the corresponding web documents. Experimental results on artificial and real document collections using well-known clustering algorithms indicate the effectiveness of the proposed approach.

We present a combined clinico-genomic knowledge discovery (CGKD) process suited for linking gene-expression (microarray) and clinical patient data. The process present a multi-strategy mining approach realized by the smooth integration of three distinct data-mining components: clustering (based on a discretized k-means approach), association rules mining, and feature-selection for selecting discrimant genes. The proposed CGKD process is applied on a real-world gene-expression profiling study (i.e., clinical outcome of breast cancer patients). Assessment of the results demonstrates the rationality and reliability of the approach.

This paper describes an ontology data service (ODS) for supporting Search and Rescue (SaR) operations. The ontological model represents various aspects of the command, communication, and organisational structure of the SaR forces and the deployment and progress of a SaR operation. Furthermore, the ontology supports the semantic indexing of multimedia documents in the context of SaR processes and activities. This ODS supports a semantically-enhanced information and communication system for SaR forces. Modelling the spatio-temporal aspects of an operation in alignment with possibly-unreliable information automatically extracted from multimedia objects, introduces a number of challenges for the field of knowledge representation and reasoning.