TourMISLOD: a Tourism Linked Data Set

The TourMISLOD dataset exposes as linked data a significant portion of the content of TourMIS, a key source of European tourism statistics data. TourMISLOD contains information about the Arrivals, Bednights and Capacity tourism indicators, recorded from 1985 onwards, about over 150 European cities and in connection to 19 major markets. Due to licensing issues, the usage of this dataset is currently limited to the TourMIS consortium. Nevertheless, a prototype application has already revealed the dataset’s usefulness for decision support

Crowdsourcing Research Opportunities: Lessons from Natural Language Processing

Although the field has led to promising early results, the use of crowdsourcing as an integral part of science projects is still regarded with skepticism by some, largely due to a lack of awareness of the opportunities and implications of utilizing these new techniques. We address this lack of awareness, firstly by highlighting the positive impacts that crowdsourcing has had on Natural Language Processing research. Secondly, we discuss the challenges of more complex methodologies, quality control, and the necessity to deal with ethical issues. We conclude with future trends and opportunities of crowdsourcing for science, including its potential for disseminating results, making science more accessible, and enriching educational programs

Dynamic Topography Information Landscapes – An Incremental Approach to Visual Knowledge Discovery

Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we introduce an incremental, scalable approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the projection stage consisting of document clustering, cluster force-directed placement and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indicate that our incremental computation approach is capable of accurately generating dynamic information landscapes

Leveraging the Wisdom of the Crowds for the Acquisition of Multilingual Language Resources

Games with a purpose are an increasingly popular mechanism for leveraging the wisdom of the crowds to address tasks which are trivial for humans but still not solvable by computer algorithms in a satisfying manner. As a novel mechanism for structuring human-computer interactions, a key challenge when creating them is motivating users to participate while generating useful and unbiased results. This paper focuses on important design choices and success factors of effective games with a purpose. Our findings are based on lessons learned while developing and deploying Sentiment Quiz, a crowdsourcing application for creating sentiment lexicons (an essential component of most sentiment detection algorithms). We describe the goals and structure of the game, the underlying application framework, the sentiment lexicons gathered through crowdsourcing, as well as a novel approach to automatically extend the lexicons by means of a bootstrapping process. Such an automated extension further increases the efficiency of the acquisition process by limiting the number of terms that need to be gathered from the game participants

Incremental and Scalable Computation of Dynamic Topography Information Landscapes

Dynamic topography information landscapes are capable of visualizing longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Acknowledging the growing number of documents to be processed by state-of-the-art Web intelligence applications, we present a scalable, incremental approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Processing steps central to incremental processing are found in the projection stage which consists of document clustering, cluster force-directed placement, and fast document positioning. We introduce two different positioning methods and compare them in an incremental setting using two different quality measures. The evaluation is performed on a set of approximately 5000 documents taken from the environmental blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate), a Web content aggregator about climate change and related environmental issues that serves static versions of the information landscapes presented in this paper as part of a multiple coordinated view representation

Media Watch on Climate Change - A Web-based Knowledge Repository to Promote Awareness and Analyze Stakeholder Communication

This paper presents the technology base and roadmap of the Climate Change Collaboratory, a Web-based platform that aims to strengthen the relations between scientists, educators, environmental NGOs, policy makers, news media and corporations - stakeholders who recognize the need for adaptation and mitigation, but differ in worldviews, goals and agendas. The collaboratory manages expert knowledge and provides a platform for effective communication and collaboration. It aims to promote awareness and environmental literacy, to facilitate networking with leading international organizations, and to bridge the science-policy gap. Rich, self-sustaining community interaction will help translate knowledge into coordinated action. Innovative survey instruments in the tradition of “games with a purpose” will create shared meaning through collaborative ontology building, and leverage social networking platforms to capture indicators of environmental attitudes, lifestyles and behaviors

Visualizing Contextual and Dynamic Features of Micropost Streams

Visual techniques provide an intuitive way of making sense of the large amounts of microposts available from social media sources, particularly in the case of emerging topics of interest to a global audience, which often raise controversy among key stakeholders. Micropost streams are context-dependent and highly dynamic in nature. We describe a visual analytics platform to handle high-volume micropost streams from multiple social media channels. For each post we extract key contextual features such as location, topic and sentiment, and subsequently render the resulting multi-dimensional information space using a suite of coordinated views that support a variety of complex information seeking behaviors. We also describe three new visualization techniques that extend the original platform to account for the dynamic nature of micro¬post streams through dynamic topography information landscapes, news flow diagrams and longitudinal cross-media analyses

Extraction and Interactive Exploration of Knowledge from Aggregated News and Social Media Content

The webLyzard media monitoring and Web intelligence platform (www.webLyzard.com) presented in this paper is a flexible tool for assessing the positioning of an organization and the effectiveness of its communications. The platform aggregates large archives of digital content from multiple stakeholders. Each week it processes millions of documents and user comments from news media, blogs, Web 2.0 platforms such as Facebook, Twitter and YouTube, and the Web sites of companies and NGOs. An interactive dashboard with trend charts and complex map projections shows how often and where information is published. It also provides a real-time account of topics that stakeholders associate with an organization. Positive or negative sentiment is computed automatically, which reflects the impact of public relations and marketing campaigns

Confidence Management for Learning Ontologies from Dynamic Web Sources

Dynamic environments require effective update mechanisms for ontologies to incorporate new knowledge. In this position paper we present a dynamic framework for ontology learning which integrates automated learning methods with rapid user feedback mechanism to build and extend lightweight domain ontologies at regular intervals. Automated methods collect evidence from a variety of heterogeneous sources and generate an ontology with spreading activation techniques, while crowdsourcing in the form of Games with a Purpose validates the new ontology elements. Special data structures support dynamic confidence management in regards to three major aspects of the ontology: (i) the incoming facts collected from evidence sources, (ii) the relations that constitute the extended ontology, and (iii) the observed quality of evidence sources. Based on these data structures we propose trend detection experiments to measure not only significant changes in the domain, but also in the conceptualization suggested by user feedbac