Evidence-based visualizations


A visual support tool designed to give healthcare providers a graphical representation of the top 10 side effects associated with a particular medicine.

“The system includes a database containing 16,340 unique drug and side-effect pairs, representing 250 common medications. A numeric score is assigned to each pair reflecting the strength of association between drug and effect. Based on these scores, the system generates graphical adverse reaction maps for any user-selected combination of drugs.”

A video demonstration can be found at: http://www.rxplore.com/AMIA/

Snake Oil: Scientific evidence for popular dietary supplements

Presented as a “balloon race”, this visualization shows the effectiveness of dietary supplements. The higher a bubble, the greater the evidence for its effectiveness. But the supplements are only effective for the conditions listed inside the bubble.

Data comes from the abstracts of over 1500 studies on PubMed (run by US National Library Of Medicine) and Cochrane.org (which hosts meta-studies of scientific research).

See the visualization on the web: Information is Beautiful

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Concept Maps: Integrating Knowledge and Information Visualization

Alberto J. Cañas, Roger Carff, Greg Hill, Marco Carvalho, Marco Arguedas, Thomas C. Eskridge, James Lott and Rodrigo Carvajal (2005). Concept Maps: Integrating Knowledge and Information Visualization. Knowledge and Information Visualization, 2426: 205-219.

1. a “well constructed” concept map,
(1) Each pair of concepts, together with their joining linking phrase, can be read as an individual statement or proposition that makes sense.
(2) Concepts and linking phrases are as short as possible, possibly single words.
(3) The structure is hierarchical and the root node of the map is a good representative of the topic of the map.

2. CmapTools: Integrating knowledge and information visualization

The CMEX Mars knowledge model consists of over 100 concept maps that are used as a means to browse and search through over 600 MBs of resources of all types.

The “Mars” concept map in the figure is the top-level map, the entry point to this knowledge model. Some concepts in the “Mars” concept map have small icons underneath them. These icons indicate that there are other resources (e.g., images, text, videos, Web pages, other concept maps) that contain additional information, refer to, or further explain that particular concept. By clicking on the “concept map” icon underneath a concept, a list of available concept maps is displayed, which the user can select and open.

3. Searching and mining the web from concept maps

Utilizing context information provided by a concept map to (1) provide more complete queries to the search engines, and (2) enhance the ranking of the results provided by the engines.

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Ontology Visualization Methods—A Survey

Katifori, A., Halatsis, C., Lepouras, G., Vassilakis, C., and Giannopoulou, E. 2007. Ontology visualization methods—A survey. ACM Comput. Surv. 39, 4, Article 10 (October 2007), 43 pages DOI = 10.1145/1287620.1287621 http://doi.acm.org/10.1145/1287620.1287621

Objective: The article aims to present a variety of ontology visualization techniques and
categorize their characteristics and features in order to assist method selection and promote future research in the area of ontology visualization.

Visualization Types: Researchers first grouped the visualizations by type –

  •  Indented list
  •  Node–link and tree,
  •  Zoomable
  •  Space-filling
  • Focus + context or distortion
  •  3D Information landscapes.

Within each of these groupings, visualizations were also characterized by the number of space dimensions they employed: 2D or 3D.

Note: The The survey was extremely comprehensive and covered many visualizations. For that reason, and because some of the particular systems and tools are no longer in existence, they are not explored in further detail here.

Advantages & Disadvantages of Specific Visualization Methods:

Indented List

  • Advantages: Simplicity, Familiarity.
  • Disadvantages: Represents a tree, not a graph so only represents inheritence and not role relations.

Node Link and Tree

  • Advantages: Good overview of hierarchical structures.
  • Disadvantages: Inefficient use of screen space.


  • Advantages: Good for browsing to specific nodes.
  • Disadvantages: Do not offer effective overview of hierarchical structure, Do not support user in forming mental model of hierarchy.

Space Filling

  • Advantages: Well-suited for tasks that involve an overview of certain properties of the ontology instances or an overview of areas with many or few nodes.
  • Disadvantages: Not particularly effective for structure related tasks.

Focus + Context or Distortion

  • Advantages: Effective at providing global overviews and displaying many nodes at once. They can be used for focusing on certain nodes and viewing their related nodes, and for quick browsing of the ontology to locate specific classes or instances.
  • Disadvantages: They do not offer a very obvious representation of
    the hierarchy structure as the user has to see the link label in order to distinguish parent from child nodes. Display/Label clutter often a problem.

3D Information Landscapes

  • Advantages: These could probably be effective for hierarchy overview related tasks, if coupled with appropriate search and filtering tools and intuitive, simple, and effective navigation mechanisms.
  • Disadvantages: They have not yet been used much in practice and there is alack of extensive evaluations as well.


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TreePlus: Interactive Exploration of Networks with Enhanced Tree Layouts

Lee, B., Parr, C. S., Plaisant, C., Bederson, B. B., Veksler, V. D., Gray, W. D., & Kotfila, C. (2006). Treeplus: Interactive exploration of networks with enhanced tree layouts. Visualization and Computer Graphics, IEEE Transactions on, 12(6), 1414–1426.
Video demo of the interface available: http://www.cs.umd.edu/hcil/treeplus/TreePlusVideo/TreePlus.html
Code (.NET) is also available: http://www.cs.umd.edu/hcil/treeplus/sourcecode.htm
This interface design is intended to allow users to explore large graphs (ie. ontologies), expanding them as needed. Thus the user gets an overview, and can zoom in to sections of the graph for a closer look, following Shneiderman’s Visual Information-Seeking Mantra: “Overview first, zoom and filter, then details-on- demand.”  Five design goals guided development:
  1. Take advantage of human perception of trees
    – previous work showed tree visualizations are broadly applicable to many audiences
  2. Make as many nodes readable as possible
    – supports users’ scanning behavior
  3. Maximize stability of layout
    – changes to the interface are predictable (unlike force-directed graphs)
  4. Offer preview before committing
    – supports information scent
  5.  Provide multi-step animations so users can follow changes
    – makes it possible for the user to follow what is changing in the interface
    – the assumption is users will be most interested in the most recently opened node
The TreePlus interface was compared to a traditional graph interface in a controlled study. As the density of data increased, the advantages of the TreePlus interface increased.
2x2x6 (2 interfaces with 2 densities of the graphs by 6 tasks) repeated- measure, within-subject design.
Participants: 28 participants, CS and Engineering students able to quickly understand graph terminology. They already understood graph and spanning tree definitions.

Dependent variables:
1. Completion Time
2. Success Rate – percentage of tasks correctly answered
3. Error- difference between the correct answer and participant response
4. User Confidence – user questionairre
Participants first received training on the first interface and the eye tracking system was calibrated for them.
Each task included 2 practice trials and from 3 to 5 timed trials depending on the tasks. Participants were allowed to ask questions during the practice trials but not during the timed ones.
TreePlus performed significantly better for most of the tasks
Participants :
– completed the tasks faster and with fewer errors
– reported higher levels of confidence in their answers
– most of them preferred TreePlus
“Results suggest that visualization and interaction techniques can effectively support incremental exploration of a graph, and can reveal the graph structure superimiposed onto a tree structure”
Participant quotes:
“I was very comfortable using it because I am used to the hierarchical structure” and “I think trees are logical and ordered arrangement of the graphs.”
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Automatic Classification and Visualization of UMLS Source Vocabularies through Semantic Group Profiles

Thai Le, Bastien Rance, Olivier Bodenreider,(2012) “Automatic Classification and Visualization of UMLS Source Vocabularies through Semantic Group Profiles”, Submitted to the AMIA Annual Symposium 2012.  draft material


  • Explore automatic methods for the classification
  • Visualization of UMLS source vocabularies based on their content


  • Selecting UMLS source vocabularies for analysis
  • Creating vectors of semantic groups for the UMLS source vocabularies
  • Assessing similarity among UMLS source vocabularies through their semantic group profiles
  • Establishing the usage-based classification of the UMLS source vocabularies
  • Comparing the usage-based and content-based classifications
  • Visualizing UMLS source vocabularies through their semantic group profiles


  • Similarity among UMLS source vocabularies through their semantic group profiles.
  • Visualizing UMLS source vocabularies through their semantic group profiles
  • Comparing the usage-based and content-based classifications


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Developing Mobile Access to Digital Collections

Mitchell, C. & Suchy, D. (2012). Developing Mobile Access to Digital Collections. DLib. January/February, 2012. http://www.dlib.org/dlib/january12/mitchell/01mitchell.html

Case studies/interviews with representatives from Duke University Libraries, Montana State University Libraries, North Carolina State University Libraries, Smithsonian Institution.

Appropriate approach for development

  • Identify intended audience and their needs.
  • Create concrete mobile use cases and compare these to desktop use cases
  • Conduct internal assessment of available resources and limitations


  • Casual institutional users and visitors
  • Smithsonian did include some some mobile services for scholars


Largely simple:

  • Learn about collection: search, explore, save, download
  • Patron needs quick access not extended research access
  • Keep project on timeline and reasonable budget
  • Deliver media optimized for the access method


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Diamond Browser: Faceted Search on Mobile Devices

 Diamond Browser: Faceted Search on Mobile Devices
Robert Capra and Jason Raitz (University of North Carolina Chapel Hill) in HCIR 2011.

Most existing faceted search interface studies focus on traditional computer display, and few studies ever explore the “tradeoffs and use of smaller-screen faceted interfaces”. This paper presents the Diamond Browser, a faceted search interface designs for mobile devices.

sbox: a query/search box (sbox)
cbox: a breadcrumb/cherry picking display of the facets included in the current query
fbox: a display of the facets available
rbox: a results display


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Mobile Digital Library in The National Library of Norway

Høivik, J. (2011). Mobile Digital Library in The National Library of Norway. Library Hi Tech News, 28(2), 1–8. doi:10.1108/07419051111135218
“The purpose of the work that I report on here is to investigate and improve access to digital assets at the Norwegian National Library based on one particular requirement: It should be achieved using a mobile phone.”
All textual materials must be deposited in the National Library, this includes “manuscripts, books, music, radio and TV programmes, film, theatre, maps, posters, pictures, photographs, internet documents and newspapers.” These materials are being digitized.
The Library created an Android App to develop and test for mobile use.
Browser based (left) and mobile (right) digital library site.
Backend of mobile uses SQL lite to store images and other media on the user’s device for use offline.
User interface:
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An Evaluation of Thesaurus-Enchanced Visual Interfaces for Multilingual Digital Libraries

Shiri, A., Ruecker, S., Doll, L., Bouchard, M. & Fiorentino, C. (2011). An Evaluation of Thesaurus-Enhanced Visual Interfaces for Multilingual Digital Libraries. In: S. Gradmann et al (eds.): TPDL 2011, LNCS 6966, pp.236-243.


Semantically rich user interfaces have the potential to assist users in formulating queries, forming context for a particular search, and exploring and gaining a comprehensive view of collections.


A comparative user evaluation of two thesaurus-enhanced visual user interfaces T-Saurus and Searchling. Twenty-five academic users carried out three search tasks on both interfaces (connected to the UNESCO digital portal). Using usability and affordance strength questionnaires, interviews, thinkalouds and observation, the researchers recorded users’ evaluations of the key components of the two interfaces (e.g. multilingual features, thesaurus browsing and search functions).

The Interfaces

The Searchling interface provides a thesaurus space, query space and document space in one screen.  The Thesaurus space includes a browsable side panel of high-level categories, next to a list of thesaurus terms. Each term has a number beside it, which indicates how many documents in the collection contain the term. When a term is queried or clicked, it moves to the top of the list and all related terms from the thesaurus appear below it. The table to the right of the Thesaurus list indicates related terms that are broader, narrower, preferred or non-preferred compared with the selected term; the user can also sort by these categories.

Fig 1: Searchling Interface

The T-Saurus search user interface makes use of visual objects, size, colour, location, zoom in and zoom out features to distinguish between various types of thesaurus terms and their relationships. Figure 2 shows a core of visual elements consisting of a set of “buckets” organized in the center of the screen. It shows the size of the buckets that represents the number of matches for a particular term, while proximity and opacity represent scope and accuracy of the term in relation to pre-established hierarchies for the query: main term, related terms, more specific, more general and synonymous terms.

Fig 2: T-Saurus Interface


  • Overall, both interfaces were found to be comprehensible to users, and the thesaurus functions were useful for broadening and narrowing the scope of research activities.
  • In general, the Searchling interface was found to be easier to use, and users’ were more likely to return to that interface for their future research.
  • Most users prefer that related, broader and narrower terms be shown along with their selected term without any additional effort.
  • Linear thinkers preferred the faceted presentation of Searchling, while visual learners preferred the graphical presentation of T-Saurus.
  • The authors speculate that T-Saurus could help transform the browsing,  searching and query formulation process.
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A Novel Approach to Visualizing and Navigating Ontologies

A Novel Approach to Visualizing and Navigating Ontologies

Motta, E., Mulholland, P., Peroni, S., d’ Aquin, M., Gomez-Perez, J., Mendez, V., & Zablith, F. (2011). A novel approach to visualizing and navigating ontologies. The Semantic Web–ISWC 2011, 470–486.
This work reports on a visualization tool, KC-Viz, for navigating ontologies in a “middle-out” approach, starting from information rich nodes. Results from a preliminary empirical study suggests KC-Viz provides increased performance for users in real-life tasks. It addresses the problem of visualizing a large ontlology on relatively limited screen real-estate:

On the one hand the information on display needs to be coarse-grained enough to provide an overview of the ontology… On the other hand, an exploration process needs to be supported, where the user can effectively home in on parts of the ontology, thus changing the level of analysis, while at the same time not losing track of the overall organization of the ontology.

Using the key concept extraction (KCE) algorithm, global and local importance of concepts is measured, allowing them to be displayed in the interface. This has the effect of providing a user of the ontology a way to gain an overview of its domain. The functionality can be particularly beneficial for new users of an ontology, a user attempting to choose from several competing ontologies, or a user creating new ontologies (and thus avoid replicating what already exists).

The authors conclude the KC-Viz system supports “information foraging” in an ontology. Thus it was found to be particularly helpful for first time users of an ontology. Users were able to learn the domain more efficiently than those who utilized the ontology navigation systems, Neon Tool Kit without visualization support or Protege with OwlViz plugin, during a controlled experiment.

The main positive subjective comments about the system were: “flexible support provided by KC-Viz to manipulate the visual displays; the abstraction power enabled by the KCE algorithm; and the value of the subtree summaries provided by KC-Viz.” The main negative comments were: “criticism of the tree layout algorithm used by KC-Viz, which does not allow display rotation and at times generates overlapping labels; the lack of transparency of the KCE algorithm, which does not allow the user to configure it, or to clarify why a node is considered more important than others; and the lack of integration between KC-Viz and reasoning/query support”

As the MCD project will include many thesauri and ontologies, it is likely we will need to generate overviews of the various domains covered. KC-Viz is a candidate technology to further this goal. We should take note of their findings that users benefited from an overview of key-concepts, and take into account the subjective user findings for the user-experience of the MCD user interface.

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