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The latest findings from the Intergovernmental Panel on Climate Change Sixth Assessment Report retells a story already told in 1979 in greater detail --- that climate change is happening because of human causes. However, the progress on addressing the climate crisis hardly corresponds to the media's nearly 40 years of news coverage. A widely cited barrier to progress is the lack of knowledge to comprehend the various interacting aspects of climate change. As a dynamic complex system, climate change is a deeply relational systems-level problem that requires communication and mental models capable of communicating system complexities. To establish the extent of relations and knowledge formations by past communications, this paper offers a methodological innovation enabling network analysis of unstructured texts. This research developed a reproducible automated semantic network approach using natural language processing and open information extraction tools to construct knowledge graphs (KGs) on climate change news coverage in 5 countries using news articles $(N = 19,684)$ from 1990 to 2020. Country comparisons were carried out cross-sectionally and longitudinally. The findings show that the KGs are structurally similar while static but differed substantially in longitudinal patterns. Countries performing better in climate commitments saw a uniform increase in the salience and embedded of concepts, while in those less progressive countries, concepts become salient sporadically. The study concludes by recommending a concerted communication model that gives equal and parallel issue-attention to the causal, action, and physical characteristic and consequence frames of climate change to facilitate understanding of the system complexity of climate change.

La isla Lesbos ( Grecia) constituye un lugar fronterizo importante de paso de flujos internacionales de migración procedentes del Asia y África y dirigidos hacia el Norte - Occidente de Europa. Este trabajo explora las percepciones de la gente de su capital, Mitilene. La investigación se centra i) en 200 encuestas y entrevistas semidirigidas, realizadas durante los meses de julio y agosto de 2015 en cuatro barrios de la ciudad, con diferenciaciones históricas y socio-espaciales y ii) en un trabajo de observación participativa entre 2015-2018. Para el procesamiento de los resultados se han generado y analizado redes semánticas de palabras que aparecen juntas en el discurso de las personas entrevistadas. Los resultados demuestran que las políticas de migración, el vínculo con el lugar de residencia y su proceso de formación social e histórica (eco-paisajes vividos), el estatus social en el trabajo, la edad y el género, influyen de una manera compleja en las percepciones de los y las habitantes de Mitilene sobre el tema de la migración. Actualmente y después del acuerdo entre EU y Turquía y los sucesivos cambios en las políticas migratorias, se nota un cambio a la opinión de los habitantes que ya se podría caracterizar como socialmente construida. Lesvos Island (Greece) is an important border crossing point for international migration flows originated from the Asia and Africa and directed towards the North - West of Europe. This study explores the perceptions of the people of Lesvos’ capital, Mytilene. The research focuses on 200 surveys and semi-directed interviews, conducted during the months of July and August 2015 in four different city neighbourhoods in terms of historical and socio-spatial aspects. To process the results, we generated and analysed semantic networks of words that appear together in the discourse of theinterviewed. Results show that the politics of migration, the links with the place of residence and its social and historic formation process, social status at work, gender and age ranks, influence the perceptions of the inhabitants of Mytilene on the issue of migration in a complex way. Currently and after the agreement between the EU and Turkey and the successive changes in migration policies, a change is noticed in the opinion of the inhabitants that could be now characterised as socially constructed

This dissertation proposes a general methodological framework for the application of computational text analysis to the study of long duration material processes of transformation, beyond their traditional application to the study of discourse and rhetorical action. Over a thin theory of the linguistic nature of social facts, the proposed methodology revolves around the compilation of term co-occurrence matrices and their projection into different representations of an hypothetical semantic space. These representations offer solutions to two problems inherent to social scientific research: that of "mapping" features in a given representation to theoretical entities and that of "alignment" of the features seen in models built from different sources in order to enable their comparison. The data requirements of the exercise are discussed through the introduction of the notion of a "narrative horizon", the extent to which a given source incorporates a narrative account in its rendering of the context that produces it. Useful primary data will consist of text with short narrative horizons, such that the ideal source will correspond to a continuous archive of institutional, ideally bureaucratic text produced as mere documentation of a definite population of more or less stable and comparable social facts across a couple of centuries. Such a primary source is available in the Proceedings of the Old Bailey (POB), a collection of transcriptions of 197,752 criminal trials seen by the Old Bailey and the Central Criminal Court of London and Middlesex between 1674 and 1913 that includes verbatim transcriptions of witness testimony. The POB is used to demonstrate the proposed framework, starting with the analysis of the evolution of an historical corpus to illustrate the procedure by which provenance data is used to construct longitudinal and cross-sectional comparisons of different corpus segments. The co-occurrence matrices obtained from the POB corpus are used to demonstrate two different projections: semantic networks that model different notions of similarity between the terms in a corpus' lexicon as an adjacency matrix describing a graph and semantic vector spaces that approximate a lower-dimensional representation of an hypothetical semantic space from its empirical effects on the co-occurrence matrix. Semantic networks are presented as discrete mathematical objects that offer a solution to the mapping problem through operation that allow for the construction of sets of terms over which an order can be induced using any measure of significance of the strength of association between a term set and its elements. Alignment can then be solved through different similarity measures computed over the intersection and union of the sets under comparison. Semantic vector spaces are presented as continuous mathematical objects that offer a solution to the mapping problem in the linear structures contained in them. This include, in all cases, a meaningful metric that makes it possible to define neighbourhoods and regions in the semantic space and, in some cases, a meaningful orientation that makes it possible to trace dimensions across them. Alignment can then proceed endogenously in the case of oriented vector spaces for relative comparisons, or through the construction of common basis sets for non-oriented semantic spaces for absolute comparisons. The dissertation concludes with the proposition of a general research program for the systematic compilation of text distributional patterns in order to facilitate a much needed process of calibration required by the techniques discussed in the previous chapters. Two specific avenues for further research are identified. First, the development of incremental methods of projection that allow a semantic model to be updated as new observations come along, an area that has received considerable attention from the field of electronic finance and the pervasive use of Gentleman's algorithm for matrix factorisation. Second, the development of additively decomposable models that may be combined or disaggregated to obtain a similar result to the one that would have been obtained had the model being computed from the union or difference of their inputs. This is established to be dependent on whether the functions that actualise a given model are associative under addition or not.

The role of social network mass media has increased greatly in the recent years. In this paper we investigate news publication in Twitter based on Network Science approach. We analyzed news data posted using the most popular media sources to discover the most significant news over a given period of time. Significance is a qualitative property that reflects the degree of news impact on society and public opinion. We have attempted to define the threshold of significance and discover a number of news which had some significance for society in period of time from July 2014 to January 2015.

Treballs Finals del Màster en Ciència Cognitiva i Llenguatge, Facultat de Filosofia, Universitat de Barcelona, Curs: 2015-2016, Tutor: Antoni Badia Cardús, In this Master’s thesis we will approach language networks with a critical eye from a linguist’s perspective. Are language networks successful in representing key aspects of language? Are the cognitive inferences made from them sound? However, before diving into the particulars of language network research, the reader will need to bear with a somewhat hard to tread introduction (sections 2 and 3) where very basic foundations of network science are laid. In section 4 we will give a critical overview of research into semantic and syntactic networks (focusing on the latter), and in section 5 we will generate and analyze syntactic networks from English and Spanish corpora, and compare our results to those reviewed. We will end in section 6 with a reflection on modelling syntactic networks.

Internet facilita el acceso a numeroso material (vídeos, documentos, páginas web, etc.) que podría ser de utilidad en el proceso de enseñanza y aprendizaje de una asignatura. En esta experiencia se pretende involucrar a los propios alumnos en la labor de búsqueda, clasificación y evaluación de este material docente en el contexto de una asignatura del Grado de Ingeniería en Informática. La creación y gestión colaborativa de este repositorio de material se realiza utilizando herramientas desarrolladas a partir de tecnologías semánticas y de crowdsourcing. El producto final esperable es una fuente de información específica, útil y en continua actualización que ayude a los alumnos participantes a alcanzar los objetivos formativos de la asignatura. Internet provides a huge amount of different material and resources, such as videos, documents, web pages, etc., that can be useful in the teaching and learning process of any subject. In this experience, the students of the Degree of a Computer Engineering subject are involved in the tasks of: searching, classification and assessment of teaching resources and materials. The collaborative creation and management of this repository of resources is generated with specific developed software tools that are based on semantic and crowdsourcing technologies. The final product is a source of useful information, continuously updated, that contribute that the students can achieve the educational aims of the subject. Este trabajo de innovación docente es financiado por el proyecto PRAUZ-14-054 de la Convocatoria de Innovación Docente 2014-2015 de la Universidad de Zaragoza, dentro del Programa de Recursos en Abierto de la Universidad de Zaragoza (PRAUZ).

There is a long tradition in education of examination of the hidden curriculum, those elements which are implicit or tacit to the formal goals of education. This article draws upon that tradition to open up for investigation the hidden curriculum and assumptions about students and knowledge that are embedded in the coding undertaken to facilitate learning through information technologies, and emerging ‘semantic technologies’ in particular. Drawing upon an empirical study of case-based pedagogy in higher education, we examine the ways in which code becomes an actor in both enabling and constraining knowledge, reasoning, representation and students. The article argues that how this occurs, and to what effect, is largely left unexamined and becomes part of the hidden curriculum of electronically mediated learning that can be more explicitly examined by positioning technologies in general, and code in particular, as actors rather than tools. This points to a significant research agenda in technology enhanced learning.

Son yıllarda çok geniş veri tabanlarının kullanımıyla birlikte içerik tabanlıgörüntü indekslemesi ve erişimi önemli bir araştırma konusu halini almıştır.Görütü indekslenmesinde kullanılan alt düzey öznitelikler görüntülerin karmaşıkiçeriklerini yeterli olarak ifade edememektedirler. Bu çalışmada, görüntü indekslemesiiçin sahne sınıflandırmasını baz alan bir görüntü erişim sistemitanımlanmıştır. İlk olarak renk ve doğrusal çizgi yapı özellikleri kullanılarakgörüntüler bölütlenmiştir. Çizgi yapı özellikleri kullanılarak, insan yapısı gibibirörnek renklerden oluşmayan yapıların görüntülerden bölütlenmesi hedeflenmektedir.Bölütleme sonucunda elde edilen tüm bölütler k-means öbekleme algoritmasıkullanılarak öbeklendikten sonra, her görüntü içermiş olduğu bölüttürlerinin histogramıyla ifade edilmiştir. Elde edilen histogramlar üzerinde çoksınıflı ve tek sınıflı sınıflandırıcılar eğitilmiş ve her görüntü için o görüntününfarklı sınıflara ait olma olasılıkları bulunmuştur. Bir görüntü aynı anda birdenfazla sınıfa ait olabileceğinden, görüntüleri en yüksek olasılık değerini verensınıfla etiketlemek yeterli olmayabilir. Bu nedenle, görüntüler tüm sınıflara aitolma olasılıkları ile indekslenmiş ve içerik tabanlı görüntü erişimi bu indekslerkullanılarak gerçekleştirilmiştir. Görüntü erişim sistemini insan algısıyla desteklemekve anlambilimsel uçurumu en aza indirgemek için erişim senaryosuna tek sınıfsınıflandırıcı bazlı ilgililik geri beslemesi eklenmiştir. Bunun için, ilgili görüntüleriçok iyi modelleyen, ilgili olmayan görüntülerden de bir o kadar uzak duranbir hiperküre oluşturan destek vektör veri tanımlaması kullanılmıştır. Önerilenyöntemler TRECVID ve Corel veri kümelerinde denenmiş ve başarılı sonuçlarelde edilmiştir. Content-based image indexing and retrieval have become important researchproblems with the use of large databases in a wide range of areas. Because of theconstantly increasing complexity of the image content, low-level features are nolonger sufficient for image content representation. In this study, a content-basedimage retrieval framework that is based on scene classification for image indexingis proposed. First, the images are segmented into regions by using their color andline structure information. By using the line structures of the images the regionsthat do not consist of uniform colors such as man made structures are captured.After all regions are clustered, each image is represented with the histogram ofthe region types it contains. Both multi-class and one-class classification modelsare used with these histograms to obtain the probability of observing differentsemantic classes in each image. Since a single class with the highest probabilityis not sufficient to model image content in an unconstrained data set with a largenumber of semantically overlapping classes, the obtained probability values areused as a new representation of the images and retrieval is performed on thesenew representations. In order to minimize the semantic gap, a relevance feedbackapproach that is based on the support vector data description is also incorporated.Experiments are performed on both Corel and TRECVID datasets and successfulresults are obtained. 82

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2007. Thesis (Master's) -- Bilkent University, 2007. Includes bibliographical references leaves 47-49 Significant growth of multimedia data creates the need for more complicated approaches in image understanding, classification and retrieval. Semantic scene classification is a popular research area which categorizes images into semantic categories for applications like content based image retrieval. In the near future, content based image retrieval will be much more important especially for the next generation internet technologies so new approaches are very welcomed in this subject. Research has showed that classifying images using components like regions, pixels or objects is a challenging work because of the ambiguity of the visual data. The main idea about image classification is to find similarities between these components to get information about the content of the image. This thesis describes our work on classification of outdoor scenes. As the first step, regions are extracted using one-class classification and patch-based clustering algorithms. The components (pixels, regions and objects) in outdoor images have particular spatial and geometric interactions so dividing images into meaningfully clustered regions has important benefits for a detailed content analysis. For region clustering, features from different levels make specific contributions but to avoid the ambiguity, we need to use low level information and more global information together for the clustering step. Also, using spatial relationships between clustered regions, we can make inference about the detailed content of outdoor images from specific to general. Therefore, after rough segmentation, scene representations are constructed with and without spatial information. At the final step Bayesian classification approach is used with the two different scene representations. The developed methods were tested on the MIT LabelMe dataset, and the results showed that using regions and their spatial relationships improved the classification accuracy. Gökalp, Demir M.S.

Se describe un sistema de aprendizaje de reglas de primer orden, a partir de descripciones de situaciones en forma de ejemplos, que utiliza el formalismo de circunscripción como modelo formal de referencia. Los ejemplos se representan como conjunciones de disyunciones de átomos básicos. Las reglas que se obtienen se definen como aquéllas que son satisfechas por todos los modelos ( P. Z)-minimales de los ejemplos. El experto proporciona directamente el sesgo semántico. Se usan diversas heurísticas para obtener conjuntos de predicados relevantes en el conjunto de entrenamiento. que se utilizan como sesgo sintáctico para la obtención de reglas. El algoritmo de aprendizaje de reglas que se propone es el primero publicado en la literatura que puede trabajar en presencia de información disyuntiva. El sistema propuesto es fácilmente generalizable para operar cuando hay información negativa en las descripciones de los ejemplos [NUÑE91b] .El modelo de aprendizaje de reglas que se utiliza explica diferentes métodos de aprendizaje automático que en principio parecen inconexos y muy diferentes.

Purpose: Functionality of feedback in pedagogical processes has been broadly analyzed in face-to-face learning, although to a lesser extent than in the on-line learning. Narciss (2004, 2008) distinguishes two dimensions within the feedback, the semantic dimension and the structural dimension. This article aims to analyze, from the student’s perspective, the semantic dimension of feedback in a virtual learning environment (VLE). Firstly, we analyze the importance that VLE students give to feedback and its degree of personalization. Later, the usefulness that students deem feedback should have, paying special attention to each of its semantic subdimensions (Narciss, 2004). Design/methodology/approach: A survey was conducted among students of Business Administration degree of the Universitat Oberta de Catalunya (UOC). 182 students took part, separated into two groups (pilot and control). Findings: It has been proved that 90% of students give a great or very great importance to reception of feedback from their tutors, a relevance they consider to be higher than the one within a face-to-face environment. This percentage is around 75% with regard to the importance given to the level of personalization of feedback. The development of a factor analysis has revealed that usefulness of personalized feedback perceived by the students can be subsumed under two large dimensions: the one that facilitates learning (related to its semantic dimension) and the motivational one (by allowing an easier and more fluid communication with the tutor, contributing not to leave the course, etc.) The latter dimension has been also proved to be key in order to attain improvements in the students’ satisfaction with the learning process. This research was funded by the Catalan government and the UOC, within the framework of projects to Improve Teaching Quality (ITQ). Originality/value: Implementing personalized feedback has a relevant impact on the student, who values it because it makes his learning process easier, richer and more significant. Moreover, it has a clear motivational effect over the student, which had not been sufficiently evidenced by other researches. Such effect needs not to be underestimated, particularly within an on-line environment, where dropout rate is usually high.

Περίληψη: Εισαγωγή 2. Σχετική εργασία 3. Πλαίσιο υποστήριξης διαλειτουργικότητας μεταξύ OWL και XML 4. Μοντέλο αναπαράστασης γνώσης περιοχής με MPEG-7 δομές 5. Η γλώσσα ερωτήσεων MP7QL και το MP7QL μοντέλο περιγραφής προτιμήσεων αναζήτησης και φίλτρων 6. Μοντέλο υποστήριξης διαλειτουργικότητας Σημαντικής μεταξύ OWL και MPEG-7 στο πλαίσιο DS-MIRF 7. Εφαρμογές και αξιολόγηση 8. Ανακεφαλαίωση-συνεισφορά-μελλοντικές επεκτάσεις