-
Leissler, M.; Hemmje, M: Portierung und Erweiterung der Relevanzkugelmetapher des LyberWorld-Werkzeuges um interaktive Informationsvisualisierungsmechanismen (1997)
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- Year
- 1997
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Haller, S.H.M.: Mappingverfahren zur Wissensorganisation (2002)
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- 30. 5.2010 16:22:35
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Platis, N. et al.: Visualization of uncertainty in tag clouds (2016)
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- 1. 2.2016 18:25:22
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Börner, K.: Atlas of knowledge : anyone can map (2015)
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- Date
- 22. 1.2017 16:54:03
22. 1.2017 17:10:56
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Trunk, D.: Semantische Netze in Informationssystemen : Verbesserung der Suche durch Interaktion und Visualisierung (2005)
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- 30. 1.2007 18:22:41
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Palm, F.: QVIZ : Query and context based visualization of time-spatial cultural dynamics (2007)
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- Content
- Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".
-
Thissen, F.: Screen-Design-Handbuch : Effektiv informieren und kommunizieren mit Multimedia (2001)
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- Date
- 22. 3.2008 14:35:21
-
Osinska, V.; Bala, P.: New methods for visualization and improvement of classification schemes : the case of computer science (2010)
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- 22. 7.2010 19:36:46
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Jäger-Dengler-Harles, I.: Informationsvisualisierung und Retrieval im Fokus der Infromationspraxis (2013)
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- Date
- 4. 2.2015 9:22:39
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Börner, K.; Chen, C.; Boyack, K.W.: Visualizing knowledge domains (2002)
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- Abstract
- This chapter reviews visualization techniques that can be used to map the ever-growing domain structure of scientific disciplines and to support information retrieval and classification. In contrast to the comprehensive surveys conducted in traditional fashion by Howard White and Katherine McCain (1997, 1998), this survey not only reviews emerging techniques in interactive data analysis and information visualization, but also depicts the bibliographical structure of the field itself. The chapter starts by reviewing the history of knowledge domain visualization. We then present a general process flow for the visualization of knowledge domains and explain commonly used techniques. In order to visualize the domain reviewed by this chapter, we introduce a bibliographic data set of considerable size, which includes articles from the citation analysis, bibliometrics, semantics, and visualization literatures. Using tutorial style, we then apply various algorithms to demonstrate the visualization effectsl produced by different approaches and compare the results. The domain visualizations reveal the relationships within and between the four fields that together constitute the focus of this chapter. We conclude with a general discussion of research possibilities. Painting a "big picture" of scientific knowledge has long been desirable for a variety of reasons. Traditional approaches are brute forcescholars must sort through mountains of literature to perceive the outlines of their field. Obviously, this is time-consuming, difficult to replicate, and entails subjective judgments. The task is enormously complex. Sifting through recently published documents to find those that will later be recognized as important is labor intensive. Traditional approaches struggle to keep up with the pace of information growth. In multidisciplinary fields of study it is especially difficult to maintain an overview of literature dynamics. Painting the big picture of an everevolving scientific discipline is akin to the situation described in the widely known Indian legend about the blind men and the elephant. As the story goes, six blind men were trying to find out what an elephant looked like. They touched different parts of the elephant and quickly jumped to their conclusions. The one touching the body said it must be like a wall; the one touching the tail said it was like a snake; the one touching the legs said it was like a tree trunk, and so forth. But science does not stand still; the steady stream of new scientific literature creates a continuously changing structure. The resulting disappearance, fusion, and emergence of research areas add another twist to the tale-it is as if the elephant is running and dynamically changing its shape. Domain visualization, an emerging field of study, is in a similar situation. Relevant literature is spread across disciplines that have traditionally had few connections. Researchers examining the domain from a particular discipline cannot possibly have an adequate understanding of the whole. As noted by White and McCain (1997), the new generation of information scientists is technically driven in its efforts to visualize scientific disciplines. However, limited progress has been made in terms of connecting pioneers' theories and practices with the potentialities of today's enabling technologies. If the difference between past and present generations lies in the power of available technologies, what they have in common is the ultimate goal-to reveal the development of scientific knowledge.
-
Thissen, F.: Screen-Design-Manual : Communicating Effectively Through Multimedia (2003)
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- 22. 3.2008 14:29:25
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Chen, C.: CiteSpace II : detecting and visualizing emerging trends and transient patterns in scientific literature (2006)
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- 22. 7.2006 16:11:05
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Wu, K.-C.; Hsieh, T.-Y.: Affective choosing of clustering and categorization representations in e-book interfaces (2016)
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- 20. 1.2015 18:30:22
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Wu, I.-C.; Vakkari, P.: Effects of subject-oriented visualization tools on search by novices and intermediates (2018)
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- Date
- 9.12.2018 16:22:25
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Osinska, V.; Kowalska, M.; Osinski, Z.: ¬The role of visualization in the shaping and exploration of the individual information space : part 1 (2018)
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- Date
- 21.12.2018 17:22:13
-
Chowdhury, S.; Chowdhury, G.G.: Using DDC to create a visual knowledge map as an aid to online information retrieval (2004)
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- Content
- 1. Introduction Web search engines and digital libraries usually expect the users to use search terms that most accurately represent their information needs. Finding the most appropriate search terms to represent an information need is an age old problem in information retrieval. Keyword or phrase search may produce good search results as long as the search terms or phrase(s) match those used by the authors and have been chosen for indexing by the concerned information retrieval system. Since this does not always happen, a large number of false drops are produced by information retrieval systems. The retrieval results become worse in very large systems that deal with millions of records, such as the Web search engines and digital libraries. Vocabulary control tools are used to improve the performance of text retrieval systems. Thesauri, the most common type of vocabulary control tool used in information retrieval, appeared in the late fifties, designed for use with the emerging post-coordinate indexing systems of that time. They are used to exert terminology control in indexing, and to aid in searching by allowing the searcher to select appropriate search terms. A large volume of literature exists describing the design features, and experiments with the use, of thesauri in various types of information retrieval systems (see for example, Furnas et.al., 1987; Bates, 1986, 1998; Milstead, 1997, and Shiri et al., 2002).
-
Spero, S.: LCSH is to thesaurus as doorbell is to mammal : visualizing structural problems in the Library of Congress Subject Headings (2008)
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- Source
- Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
-
Batorowska, H.; Kaminska-Czubala, B.: Information retrieval support : visualisation of the information space of a document (2014)
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- Source
- Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
-
Graphic details : a scientific study of the importance of diagrams to science (2016)
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- Content
- As the team describe in a paper posted (http://arxiv.org/abs/1605.04951) on arXiv, they found that figures did indeed matter-but not all in the same way. An average paper in PubMed Central has about one diagram for every three pages and gets 1.67 citations. Papers with more diagrams per page and, to a lesser extent, plots per page tended to be more influential (on average, a paper accrued two more citations for every extra diagram per page, and one more for every extra plot per page). By contrast, including photographs and equations seemed to decrease the chances of a paper being cited by others. That agrees with a study from 2012, whose authors counted (by hand) the number of mathematical expressions in over 600 biology papers and found that each additional equation per page reduced the number of citations a paper received by 22%. This does not mean that researchers should rush to include more diagrams in their next paper. Dr Howe has not shown what is behind the effect, which may merely be one of correlation, rather than causation. It could, for example, be that papers with lots of diagrams tend to be those that illustrate new concepts, and thus start a whole new field of inquiry. Such papers will certainly be cited a lot. On the other hand, the presence of equations really might reduce citations. Biologists (as are most of those who write and read the papers in PubMed Central) are notoriously mathsaverse. If that is the case, looking in a physics archive would probably produce a different result.
-
Information visualization in data mining and knowledge discovery (2002)
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- Date
- 23. 3.2008 19:10:22
