Search (47 results, page 2 of 3)

  • × theme_ss:"Visualisierung"
  • × type_ss:"a"
  1. Eibl, M.: Visualisierung der Recherche in Dokument-Retrievalsystemen : Methoden und Lösungen (1999) 0.00 
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  2. Ohly, H.P.: Erstellung und Interpretation von semantischen Karten am Beispiel des Themas 'Soziologische Beratung' (2004) 0.00 
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  3. Vizine-Goetz, D.: DeweyBrowser (2006) 0.00 
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  4. Burkhard, R.: Knowledge Visualization : Die nächste Herausforderung für Semantic Web Forschende? (2006) 0.00 
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  5. Jäger-Dengler-Harles, I.: Informationsvisualisierung und Retrieval (2015) 0.00 
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  6. Wild, F.: Visuelle Verfahren im Information Retrieval (2005) 0.00 
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  7. Passath, C.: Information-Panels : Die Informationsvermittler der Zukunft (2005) 0.00 
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  8. Aris, A.; Shneiderman, B.; Qazvinian, V.; Radev, D.: Visual overviews for discovering key papers and influences across research fronts (2009) 0.00 
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  9. Brockelmann, M.; Wolff, C.: 3D-Visualisierungen : Potenziale in Forschung und Lehre im Kontext von Informationswissenschaft und Medieninformatik (2013) 0.00 
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  10. Soylu, A.; Giese, M.; Jimenez-Ruiz, E.; Kharlamov, E.; Zheleznyakov, D.; Horrocks, I.: Towards exploiting query history for adaptive ontology-based visual query formulation (2014) 0.00 
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  11. Albertson, D.: Visual information seeking (2015) 0.00 
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  12. Jaklitsch, M.: Informationsvisualisierung am Beispiel des Begriffs Informationskompetenz : eine szientometrische Untersuchung unter Verwendung von BibExcel und VOSviewer (2016) 0.00 
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  13. Stadtbibliothek Reutlingen: Web-Auftritt komplett mit Web-Opac Premium von BOND realisiert (2005) 0.00 
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  14. Schek, M.: Automatische Klassifizierung in Erschließung und Recherche eines Pressearchivs (2006) 0.00 
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  15. Zhang, J.; Mostafa, J.; Tripathy, H.: Information retrieval by semantic analysis and visualization of the concept space of D-Lib® magazine (2002) 0.00 
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    Abstract
    In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
    Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.
    Content
    The JAVA applet is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. A prototype of this interface has been developed and is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. The D-Lib search interface is available at <http://www.dlib.org/Architext/AT-dlib2query.html>.
    Source
    D-Lib magazine. 8(2002) no.10, x S
  16. Eibl, M.; Mandl, T.: ¬Die Qualität von Visualisierungen : eine Methode zum Vergleich zweidimensionaler Karten (2004) 0.00 
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  17. Reiterer, H.; Jetter, H.-C.: ¬Das Projekt Mediovis : visuelle Exploration digitaler Bibliotheken (2007) 0.00 
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  18. Huang, S.-C.; Bias, R.G.; Schnyer, D.: How are icons processed by the brain? : Neuroimaging measures of four types of visual stimuli used in information systems (2015) 0.00 
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  19. Cao, N.; Sun, J.; Lin, Y.-R.; Gotz, D.; Liu, S.; Qu, H.: FacetAtlas : Multifaceted visualization for rich text corpora (2010) 0.00 
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  20. Brantl, M.; Ceynowa, K.; Meiers, T.; Wolf, T.: Visuelle Suche in historischen Werken (2017) 0.00 
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