Deliverable D2.3 Specification of Web mining process for hypervideo concept identification

This deliverable presents a state-of-art and requirements analysis report for the web mining process as part of the WP2 of the LinkedTV project. The deliverable is divided into two subject areas: a) Named Entity Recognition (NER) and b) retrieval of additional content. The introduction gives an outline of the workflow of the work package, with a subsection devoted to relations with other work packages. The state-of-art review is focused on prospective techniques for LinkedTV. In the NER domain, the main focus is on knowledge-based approaches, which facilitate disambiguation of identified entities using linked open data. As part of the NER requirement analysis, the first tools developed are described and evaluated (NERD, SemiTags and THD). The area of linked additional content is broader and requires a more thorough analysis. A balanced overview of techniques for dealing with the various knowledge sources (semantic web resources, web APIs and completely unstructured resources from a white list of web sites) is presented. The requirements analysis comes out of the RBB and Sound and Vision LinkedTV scenarios

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach

Climate Adaptation and Resilience Across Scales

Climate Adaptation and Resilience Across Scales provides professionals with guidance on adapting the built environment to a changing climate. This edited volume brings together practitioners and researchers to discuss climate-related resilience from the building to the city scale. This book highlights North American cases that deal with issues such as climate projections, public health, adaptive capacity of vulnerable populations, and design interventions for floodplains, making the content applicable to many locations around the world. The contributors in this book discuss topics ranging from how built environment professionals respond to a changing climate, to how the building stock may need to adapt to climate change, to how resilience is currently being addressed in the design, construction, and operations communities. The purpose of this book is to provide a better understanding of climate change impacts, vulnerability, and resilience across scales of the built environment. Architects, urban designers, planners, landscape architects, and engineers will find this a useful resource for adapting buildings and cities to a changing climate

Temporal Dimension of Text: Quantification, Metrics and Features

The time dimension is so inherently bound to any information space that it can hardly be ignored when describing the reality, nor can be disregarded in interpreting most information. In the pressing need to search and classify a larger amount of unstructured data with better accuracy, the temporal dimension of text documents is becoming a crucial property for information retrieval and text mining tasks. Of all the features that characterize textual information, the time dimension is still not fully regarded, despite its richness and diversity. Temporal information retrieval is still in its infancy, while time features of documents are barely taken into account in text classification. The temporal aspects of text can be used to better interpret the relative truthiness and the context of old information, and to determine the relevance of a document with respect to information needs and categories. In this research, we first explore the temporal dimension of text collections in a large scale study on more than 30 million documents, quantifying its extent and showing its peculiarities and patterns, such as the relation between the creation time of documents and the mentioned time. Then we define a comprehensive and accurate representation of the temporal aspects of documents, modeling ad-hoc temporal similarities based on metric distances between time intervals. Results of evaluation show taking into account the temporal relevance of documents yields a significant improvement in retrieval effectiveness, over both implicit and explicit time queries, and a gain in classification accuracy when temporal features are involved. By defining a set of temporal features to comprehensively describe the temporal scope of text documents, we show their significant relation to topical categories and how these proposed features are able to categorize documents, improving the text categorization tasks in combination with ordinary terms frequencies features

SELF-STUDY REPORT Appendix 15: Libraries Faculty Vitae

294 page

Closing the gateways of democracy : cities and the militarization of protest policing

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2005.Includes bibliographical references (p. [139]-147).In the era of globalization, cities function as 'gateways of democracy,' the spaces and places where the civil society literally 'marches through' in order to deliver oppositional claims into the global arena. However, this paper documents a broad, increasing pattern of political repression directed against peaceful protest in US cities, signifying that important avenues for democratic participation may indeed be closing.by Gan Golan.M.C.P