-
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
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Platis, N. et al.: Visualization of uncertainty in tag clouds (2016)
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- Date
- 1. 2.2016 18:25:22
-
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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Christoforidis, A.; Heuwing, B.; Mandl, T.: Visualising topics in document collections : an analysis of the interpretation process of historians (2017)
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Oh, K.E.; Halpern, D.; Tremaine, M.; Chiang, J.; Silver, D.; Bemis, K.: Blocked: when the information is hidden by the visualization (2016)
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- Abstract
- This study investigated how people comprehend three-dimensional (3D) visualizations and what properties of such visualizations affect comprehension. Participants were asked to draw the face of a 3D visualization after it was cut in half. We videotaped the participants as they drew, erased, verbalized their thoughts, gestured, and moved about a two-dimensional paper presentation of the 3D visualization. The videorecords were analyzed using a grounded theory approach to generate hypotheses related to comprehension difficulties and visualization properties. Our analysis of the results uncovered three properties that made problem solving more difficult for participants. These were: (a) cuts that were at an angle in relation to at least one plane of reference, (b) nonplanar properties of the features contained in the 3D visualizations including curved layers and v-shaped layers, and (c) mixed combinations of layers. In contrast, (a) cutting planes that were perpendicular or parallel to the 3D visualization diagram's planes of reference, (b) internal features that were flat/planar, and (c) homogeneous layers were easier to comprehend. This research has direct implications for the generation and use of 3D information visualizations in that it suggests design features to include and avoid.
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Wu, Y.; Bai, R.: ¬An event relationship model for knowledge organization and visualization (2017)
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- Abstract
- An event is a specific occurrence involving participants, which is a typed, n-ary association of entities or other events, each identified as a participant in a specific semantic role in the event (Pyysalo et al. 2012; Linguistic Data Consortium 2005). Event types may vary across domains. Representing relationships between events can facilitate the understanding of knowledge in complex systems (such as economic systems, human body, social systems). In the simplest form, an event can be represented as Entity A <Relation> Entity B. This paper evaluates several knowledge organization and visualization models and tools, such as concept maps (Cmap), topic maps (Ontopia), network analysis models (Gephi), and ontology (Protégé), then proposes an event relationship model that aims to integrate the strengths of these models, and can represent complex knowledge expressed in events and their relationships.
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Osinska, V.; Bala, P.: New methods for visualization and improvement of classification schemes : the case of computer science (2010)
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- Date
- 22. 7.2010 19:36:46
-
Jäger-Dengler-Harles, I.: Informationsvisualisierung und Retrieval im Fokus der Infromationspraxis (2013)
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- Date
- 4. 2.2015 9:22:39
-
Aletras, N.; Baldwin, T.; Lau, J.H.; Stevenson, M.: Evaluating topic representations for exploring document collections (2017)
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- Abstract
- Topic models have been shown to be a useful way of representing the content of large document collections, for example, via visualization interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a term list; that is the top-n words with highest conditional probability within the topic. Other topic representations such as textual and image labels also have been proposed. However, there has been no comparison of these alternative representations. In this article, we compare 3 different topic representations in a document retrieval task. Participants were asked to retrieve relevant documents based on predefined queries within a fixed time limit, presenting topics in one of the following modalities: (a) lists of terms, (b) textual phrase labels, and (c) image labels. Results show that textual labels are easier for users to interpret than are term lists and image labels. Moreover, the precision of retrieved documents for textual and image labels is comparable to the precision achieved by representing topics using term lists, demonstrating that labeling methods are an effective alternative topic representation.
-
Wen, B.; Horlings, E.; Zouwen, M. van der; Besselaar, P. van den: Mapping science through bibliometric triangulation : an experimental approach applied to water research (2017)
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-
Yan, B.; Luo, J.: Filtering patent maps for visualization of diversification paths of inventors and organizations (2017)
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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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- Date
- 20. 1.2015 18:30:22
-
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
-
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
-
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.
