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Knowledge Visualisation: Towards a New Discipline and its Fields of Application


Abstract and Figures

In this paper, we establish the concept of knowledge visualization and review the state-of-theart in this emergent domain. We define the concept and differentiate it from information visualization. We describe select background disciplines and potential application fields. Various knowledge visualization types are distinguished and examples of their real-life application are provided and discussed. Implications and future trends and perspectives are outlined.
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Paper # 2/2004, July 2004-07-28 Version 2.6
Knowledge Visualization
Towards a New Discipline and its Fields
of Application
Martin J. Eppler is a professor of information and communication management at the University of
Lugano where he teaches Strategy and Organization, Industry Analysis and Knowledge Management. His
current research focuses on “Knowledge Communication between Domain Experts and Decision Makers”
( He has published over 50 scientific articles and seven books, of
which the last deals with the subject of knowledge communication in organizations.
Remo A. Burkhard, dipl.Arch ETH., is the head of the competence center Knowledge Visualization at
the University of St. Gallen’s =mcm institute for media and communications management
( He is co-author of the Science City project of the ETH Zurich. He is also the
founder and head of vision of vasp datatecture, a firm that specializes in visualizing complex business
Table of Contents
1. Introduction............................................................................................................................................ 3
The Concept of Knowledge Visualization............................................................................................... 3
Differences between Knowledge Visualization and Information Visualization...................................... 4
Application Areas within Knowledge Management................................................................................ 4
2. Background ............................................................................................................................................ 7
Information Visualization........................................................................................................................ 7
Visual Cognition and Perception............................................................................................................. 7
Visual Communication Studies ............................................................................................................... 8
3. A Framework for Knowledge Visualization ........................................................................................ 8
4. Formats and Examples of Knowledge Visualization ........................................................................ 10
Heuristic Sketches: Creating new Insights in Groups ........................................................................... 10
Conceptual Diagrams: Structuring Information and Illustrating Relationships..................................... 11
Visual Metaphors: Relating Domains to Improve Understanding......................................................... 13
Knowledge Animations: Dynamic and Interactive Visualizations........................................................ 15
Knowledge Maps: Navigating and Structuring Expertise ..................................................................... 17
Scientific Charts: Visualizing Domain Knowledge and Intellectual Structures .................................... 20
5. Conclusion and Outlook...................................................................................................................... 22
A Model for Knowledge Visualization.................................................................................................. 22
Knowledge Visualization as Mediator between Strategic Management, Advertising and Marketing... 23
Knowledge Ambienting: Moving Knowledge Visualization off the screen.......................................... 23
References................................................................................................................................................. 25
Table of Figures
Figure 1: A sample knowledge communication tool to foster knowledge creation ..................................... 6
Figure 2: Three different perspectives of the knowledge visualization framework..................................... 9
Figure 3: Freud's heuristic sketch as a catalyst for theory development.................................................... 11
Figure 4: Leonardo da Vinci's heuristic sketch to illustrate the main mechanism of a machine................ 11
Figure 5: An ad-hoc heuristic sketch in an urban planning workshop....................................................... 11
Figure 6: An overview of conceptual diagrams (schematic depictions of abstract ideas which use
standardized shapes to structure information, convey insights and illustrate relations) ............ 12
Figure 7: The Toulmin chart as an example of a knowledge-intensive conceptual diagram..................... 13
Figure 8: The Negotiation Bridge: A visual metaphor that outlines a negotiation method........................ 14
Figure 9: The Market Stairs that lead to successful market entries............................................................ 15
Figure 10: An Interactive Visualization helps to supervise the New York Stock Exchange. ................... 16
Figure 11: The interactive parameter ruler enables teams to explore alternate in real-time ...................... 17
Figure 12: The tube map visualization (1,2 x 2,4 meter) presents an overview and details on a project.
Each line represents one target group, each station a project milestone. Each line (target group)
stops at the stations (milestones) where the target groups are involved. The stations are tagged
with descriptions, dates or instructions...................................................................................... 18
Figure 13: A zoom-in presents an individual and a collective milestone................................................... 19
Figure 14: A Metro Map used for Project Knowledge Documentation ..................................................... 20
Figure 15: A visual literature review diagram on information overload.................................................... 21
In this paper, we establish the concept of knowledge visualization and review the state-of-the-
art in this emergent domain. We define the concept and differentiate it from information
visualization. We describe select background disciplines and potential application fields.
Various knowledge visualization types are distinguished and examples of their real-life
application are provided and discussed. Implications and future trends and perspectives are
Key Words: knowledge visualization, knowledge maps, sketches, conceptual diagrams,
knowledge management, information visualization, cognition, metaphors
1. Introduction
Making knowledge visible so that it can be better accessed, discussed, valued or generally
managed is a long standing objective in knowledge management (see Sparrow, 1998).
Knowledge maps, knowledge cartographies, or knowledge landscapes are often heard terms that
are nevertheless rarely defined, let alone demonstrated or described in detail. In this
contribution, we review the state-of-the-art in the area of knowledge visualization and describe
its background and perspectives. We define the concept and differentiate it from other
approaches, such as information visualization or visual communication. Core knowledge
visualization types, such as conceptual diagrams or visual metaphors, are distinguished and
examples of their application in business are shown and discussed. Implications for research and
practice are summarized and future trends in this domain are outlined.
The Concept of Knowledge Visualization
Generally speaking, the field of knowledge visualization examines the use of visual
representations to improve the creation and transfer of knowledge between at least two
people. Knowledge visualization thus designates all graphic means that can be used to
construct and convey complex insights. Beyond the mere transport of facts, knowledge
visualization aims to transfer insights, experiences, attitudes, values, expectations,
perspectives, opinions and predictions, and this in a way that enables someone else to
re-construct, remember and apply these insights correctly. Examples of knowledge
visualization formats are complex, reasoned and often theory-driven conceptual
diagrams (such as Gartner’s magic quadrants or hype curve, Michael Porter’s five
forces chart or Nonaka’s SECI matrix, see Nonaka et al., 2000), concept maps (such as
Allen Novak’s concept mapping method, see Lansing, 1998), interactive visual
metaphors (such as an iceberg of organizational culture or a personnel selection funnel),
or knowledge maps (such as Roche’s knowledge application map of the new drug
approval process, see Wurman, 1996, p. 172). It seems justified to refer to these graphic
formats as knowledge visualizations as both their content and their format are distinct
from that of regular visual depictions. In terms of their content, they capture not just
(descriptive) facts or numbers, but rather (prescriptive and prognostic) insights,
principles and relations. In terms of format, knowledge visualizations rely on indirect
communication that triggers sense making activities in the viewer and motivate him or
her to complete the picture him- or herself. Thus, the ‘what’ and the ‘how’ of
knowledge visualization differs from information visualization, these differences are
further described in the following section.
Differences between Knowledge Visualization and Information Visualization
A related field and precursor to knowledge visualization is information visualization.
Information visualization is a rapidly advancing field of study both in terms of academic
research and practical applications (Bertin, 1974; Card et al., 1999; Chen, 1999a;
Spence, 2000; Ware, 2000). Card et al. (1999) define information visualization, as "...
the use of computer-supported, interactive, visual representations of abstract data to
amplify cognition". This definition is well established and represents a broad consensus
among computer scientists active in this field. What is still missing in the current
literature, however, is a systematic discussion on the potential of visualizations as a
medium for the transfer of knowledge as well as the integration of non-computer based
visualization methods, as architects, artists, and designers use them. Information
visualization and knowledge visualization are both exploiting our innate abilities to
effectively process visual representations, but the way of using these abilities differs in
both domains: Information visualization aims to explore large amounts of abstract
(often numeric) data to derive new insights or simply make the stored data more
accessible. Knowledge visualization, in contrast, aims to improve the transfer and
creation of knowledge among people by giving them richer means of expressing what
they know. While information visualization typically helps to improve information
retrieval, access and presentation of large data sets – particularly in the interaction of
humans and computers – knowledge visualization primarily aims at augmenting
knowledge-intensive communication between individuals, for example by relating new
insights to already understood concepts, as in the case of visual metaphors. This visual
communication of knowledge is relevant for several areas within knowledge
management, as described below.
Application Areas within Knowledge Management
Knowledge Visualization helps to solve several predominant, knowledge-related
problems in organizations:
First, the omnipresent problem of knowledge transfer (or rather knowledge asymmetry
and how it can be overcome by transfer). Knowledge visualization offers a systematic
approach how visual representations can be used for the transfer of knowledge in order
to increase its speed and its quality. The transfer of knowledge occurs at various levels:
among individuals, from individuals to groups, between groups, and from individuals
and groups to the entire organization. At each of these levels, knowledge visualization
can serve as a conceptual bridge, linking not only minds, but also departments and
professional groups. Gupta and Govindarajan (2000) have examined knowledge transfer
in organizations and they have found that one key issue is how recipients not only
acquire and assimilate but also use knowledge (Cohen and Levinthal, 1990). To do so,
knowledge must be recreated in the mind of the receiver (El Sawy et al., 1997). This
depends on the recipient’s cognitive capacity to process the incoming stimuli (Vance
and Eynon, 1998). Thus, the person responsible for the transfer of knowledge not only
needs to convey the relevant knowledge at the right time to the right person, he or she
also needs to convey it in the right context and in a way that it can ultimately be used.
To achieve theses tasks, text and IT-based methods can be employed (e.g., discussion
boards, databases, corporate directories, intelligent agent software, etc.). However, the
capacities of our visual channel are rarely fully exploited in these applications (be it as
an interface to make knowledge accessible or as a way structure the documented or
referenced knowledge itself). In this context, visualization can also facilitate inter-
functional knowledge communication, as the communication between different
stakeholders and experts with different professional backgrounds is a major problem in
organizations. Knowledge visualization offers solutions to solve this problem mainly by
making differing basic assumptions visible and communicable and by providing
common contexts (visual frameworks) that help to bridge differing backgrounds.
As a second application area within knowledge management, knowledge visualization
offers great potential for the creation of new knowledge, thus enabling innovation.
Knowledge visualization offers methods to use the creative power of imagery and the
possibility of fluid re-arrangements and changes. It enables groups to create new
knowledge, for instance by use of heuristic sketches or rich graphic metaphors. Unlike
text, these graphic formats can be quickly and collectively changed and thus propagate
the rapid and joint improvement of ideas. The figure below depicts such a visual
knowledge communication tool that can be used for idea generation, elaboration and
selection – the Ideaquarium (here used to develop a new advertising strategy). Each
contributed idea is represented by a fish. The larger the fish, the more people support a
proposed idea. The color of the fish indicates the person who has proposed an idea. The
criteria by which the ideas are then assessed are visualized as piranha-like fish that
approach idea-fishes to see whether the criterion is actually matched or not (a new
criterion is about to be entered unto the unlabeled fish). The higher a fish rises, the more
pragmatic an idea. Clusters of fish or fish connected by a plant indicate related ideas.
The shells on the aquarium’s ground indicate contextual starting points; the three rocks
indicate thinking vectors that should guide the idea generation process.
Figure 1: A sample knowledge communication tool to foster knowledge creation
A third, more general, application motive of knowledge visualization is its use as an
effective strategy against information overload: Information overload (see Eppler,
Mengis, 2004) is a major problem in knowledge-intensive organizations and in an
information society in general. Knowledge visualizations help to compress large
amounts of information with the help of analytical frameworks, theories, and models
that absorb complexity and render it accessible. This can be a vital prerequisite for the
three application domains mentioned previously (transfer, creation, communication).
Although these application fields have existed for numerous years, the potential of
visual representations is often lost because there is little assistance for non-professional
visualizers to make use of the power of complex visualization. Therefore a conceptual
framework should be developed that enables practitioners to better use and apply visual
representations of knowledge. Steps towards such a framework are presented in section
three. In the next section, we briefly outline relevant background areas that have paved
the way for knowledge visualization as a new discipline.
2. Background
The field of knowledge visualization is an emerging one, merging approaches from
information visualization, didactic techniques, visual cognition and visual
communication research, as well as more practical approaches, such as business
diagramming or visual programming languages.
Information Visualization
Information visualization is a rapidly advancing field of study (Card et al., 1999;
Chen, 1999a; Spence, 2000; Ware, 2000). As stated earlier, Card et al. (1999) define it,
as "... the use of computer-supported, interactive, visual representations of abstract data
to amplify cognition". Since the 1990ies, new visualization methods allow to explore
data by offering different methods to achieve the sequence discussed by Shneideman
(1996): "overview first, zoom in and filter, then show details on demand". Examples of
such applications are Tree Maps (Johnson and Shneiderman, 1991; Shneiderman, 1992),
Cone Trees (Robertson and Mackinlay, 1991), Hyperbolic 3D (Munzner, 1998).
Information Visualization helps for information exploration and visual information
seeking. Information exploration is the interactive browsing and analysis of data with a
visual interface, which allows identifying trends or outliners. It is useful complementary
to standard database queries and information retrieval approaches if little is known on
the data and if the goals are not clear. Visual information seeking combines the visual
representations of information with dynamic user control techniques, which helps to
constantly explore visual patterns while exploring the data and on the base of these
insights reformulate the goals.
Visual Cognition and Perception
A majority of our brain’s activity deals with processing and analyzing visual images.
Several empirical studies show that visual representations are superior to verbal-
sequential representations in different tasks (Larkin and Simon, 1987; Glenberg and
Langston, 1992; Bauer and Johnson-Laird, 1993; Novick, 2001). Miller (1956) reports
that a human’s input channel capacity is greater when visual abilities are used. Our
brain has a strong ability to identify patterns, which is examined in Gestalt psychology
(Koffka, 1935). Research on visual imagery (Kosslyn, 1980; Shepard and Cooper,
1982) suggests that visual recall seems to be better than verbal recall. It is not clear how
images are stored and recalled, but it is clear that humans have a natural ability to use
images. Instructional psychology and media didactics investigate the learning outcomes
of text-alone versus text-picture: (Mandl and Levin, 1989) present different results in
knowledge acquisition from text and pictures. Weidenmann (1989) explores aspects of
illustrations in the learning process. Cognitive neuroscience discusses the underlying
cognitive components of picture processing (Farah, 2000). The use of visual
representations are helpful to functions of visual communicate different knowledge
Visual Communication Studies
Different isolated research fields contribute valuable results for the visual
communication of knowledge. These are contributions in the field of visualizing
information in print (Bertin, 1974; Tufte, 1990; Tufte, 1997), cognitive art and
hypermedia design (Horn, 1998), information architecture (Wurman, 1996) and
contributions in the field of graphic design, interface design, interaction design and
human computer interaction. From a theoretical perspective, there are different
contributions that help to improve the transfer of knowledge, particularly
communication science (Fiske, 1982), visual communication sciences (Newton, 1998;
Stonehill, 1995) the psychology of learning (Weidenmann, 1989), and cognitive
psychology (Farah, 2000). These contributions show how visual representations affect
our social cognition processes both positively (improving understanding) and negatively
(manipulating perception and interpretation). Many systematic approaches that examine
visualization in communication, however, have so far been rooted in the mass media
sector. They have primarily described how newspapers and television use graphic
representations to convey meaning. How to use such formats actively for knowledge
transfer is rarely discussed in these contributions (for an overview on visual
communication studies see Müller, 2003).
We use insights from these and other domains to categorize the main application
parameters of knowledge visualization in the next section.
3. A Framework for Knowledge Visualization
For an effective transfer and creation of knowledge through visualization, at least three
perspectives should be considered. These perspectives answer three key questions with
regard to visualizing knowledge, namely:
1. What type of knowledge is visualized?
2. Why should that knowledge be visualized?
3. How is the knowledge visualized?
Listing possible answers to these key questions leads us to a first conceptual framework
that can provide an overview of the knowledge visualization field.
Knowledge Type
Visualization Goal
Visualization Format
Know-what Sharing or Transferring
(clarification, elicitation,
Heuristic Sketches (e.g. ad-
hoc drawings)
Know-how Creating (discovery,
Conceptual Diagrams (e.g.,
Toulmin or process
Know-why Learning (acquisition,
Visual Metaphors (e.g., a
tree, bridge, juggling, etc.)
Know-where Codifying (documentation,
Knowledge Animations (e.g.,
ruler, mixer, etc.)
Know-who Finding (e.g., experts,
documents, groups)
Knowledge Maps (e.g.,
knowledge structure maps)
Assessing / Evaluating
(knowledge rating)
Scientific Charts (e.g., co-
citation webs)
Figure 2: Three different perspectives of the knowledge visualization framework
The Knowledge Type Perspective aims to identify the type of knowledge that needs to
be transferred. Different types of knowledge are described in the knowledge
management literature. For our framework we distinguished five types of knowledge:
Declarative knowledge (Know-what), procedural knowledge (know-how), experimental
knowledge (know-why), orientational knowledge (know-where), individual knowledge
(know-who) (for this distinction see for example Alavi & Leidner, 2001). Today, no
classification exists that links visualization formats and these knowledge types. There is
thus no validated prescriptive framework that offers specific representation formats for r
particular knowledge types (the horizontal links, drawn as dotted arrows in the above
framework are still hypotheses).
The Visualization Motive Perspective distinguishes several reasons why a visual
knowledge representation is used. Motives for knowledge visualization use that can be
anticipated are knowledge sharing through visual means, knowledge crafting or
creation, learning from visuals, codifying past experiences visually for future users or
mapping knowledge (Vail, 1999) so that experts, for example within a large
organization, can be more easily identified.
The Visualization Format Perspective structures the visualization methods to six main
groups: heuristic sketches, conceptual diagrams, visual metaphors, knowledge
animations, knowledge maps and scientific formats. This distinction is derived from
specific formats of representing insight and from different types of insight: Heuristic
sketches represent the main idea, are atmospheric and help to quickly visualize an idea,
thus an unstable (heuristic) format for unstable knowledge. Heuristic sketches support
reasoning and arguing and allow room for one’s own interpretations. Conceptual
diagrams, by contrast, are abstract, schematic representations used to explore structural
relationships among parts. They help to reduce complexity, amplify cognition, explain
causal relationships and to structure information. The type of knowledge that is
conveyed by conceptual diagrams is analytic and their format is thus highly structured
and systematic. Visual metaphors combine the creative leap of sketches with the
analytic rationality of conceptual diagrams and employ graphic metaphors to structure
information and convey normative knowledge through the connotations of the
employed metaphor. The knowledge that is conveyed is often (in contrast to the
reasoning conveyed through diagrams) procedural, thus motivating to apply the
knowledge is a key ingredient of such visual metaphors. Knowledge animations also
convey procedural knowledge, but not in a static manner like visual metaphors but
through interactive animation. Knowledge maps do not directly represent knowledge but
rather reference it, though the use of cartographic conventions. Scientific charts finally,
display as content scientific knowledge, such as publications, and show how they are
related in terms of mutual influence. These six visualization formats can be matched
with adequate knowledge types and motives. Knowledge maps, for example can help to
visualize know-who and thus make experts easier to locate. Visual metaphors can foster
learning by displaying experiences (know-why) in an accessible way. Conceptual
diagrams, for example process charts, can depict know-how (procedural knowledge) in
order to share best practices. Heuristic sketches (as shown below) can help to create
new knowledge of various forms.
4. Formats and Examples of Knowledge Visualization
Having outlined the key questions of knowledge visualization, we show in this section
how they can be answered for specific application contexts.
Heuristic Sketches: Creating new Insights in Groups
Heuristic Sketches are drawings that are used to assist the group reflection and
communication process by making unstable knowledge explicit and debatable.
Generally a sketch is defined as: “Traditionally a rough drawing or painting in which
an artist notes down his preliminary ideas for a work that will eventually be realized
with greater precision and detail.1 In the context of knowledge management we call
these sketches heuristic sketches. The main benefits of heuristic sketches are: (1) they
represent the main idea and key features of a preliminary study. (2) They are
atmospheric, versatile and accessible. (3) They are fast and help to quickly visualize an
idea. (4) The use of a pen on a flipchart attracts the attention towards the communicator.
(5) Heuristic Sketches allow room for one’s own interpretations and foster the creativity
in groups. Figure 3-4 present different examples of heuristic sketches:
Figure 3: Freud's heuristic sketch as a catalyst for theory development.
Figure 4: Leonardo da Vinci's heuristic sketch to illustrate the main mechanism of a machine.
Figure 5: An ad-hoc heuristic sketch in an urban planning workshop increases the communication quality and fosters
the creativity in groups.
Conceptual Diagrams: Structuring Information and Illustrating Relationships
Conceptual Diagrams as seen in Figure 4 are schematic depictions of abstract ideas with
the help of standardized shapes (such as arrows, circles, pyramids or matrices) used to
structure information and illustrate relationships. Garland (1979) defines a diagram as a
visual language sign having the primary purpose of denoting function and/or
relationship”. For the transfer and creation of knowledge conceptual diagrams help to
make abstract concepts accessible, to reduce the complexity to the key issues (Huff,
1990), to amplify cognition and to discuss relationships.
1 Sketch. Encyclopædia Britannica. Retrieved August 4, 2003, from Encyclopædia Britannica Premium Service.
Figure 6: An overview of conceptual diagrams (schematic depictions of abstract ideas which use standardized shapes
to structure information, convey insights and illustrate relations) Source: Eppler, 2003
The figure above summarizes many commonly used (quantitative and qualitative)
diagrams, such as bar, line and pie charts (A and B), matrices (C), Spectrum charts (D),
cycles (H), concentric spheres (I), Mind Maps (J), process (K) and fishbone charts (L),
pyramids (M), relevance trees (N), Venn (O), network (P), and Sankey diagrams (G),
synergy maps (F), radar charts (E)., or the commonly used coordinate systems (Q and
An example of a knowledge-intensive diagram is the Toulmin chart (Figure 5), based on
the argumentation theory of Steven Toulmin (1964). Such a chart helps to break down
an argument into different parts (such as claim, reasons, and evidence) which is useful
when evaluating the validity of a claim. The parts of a reasoned argument can be
effectively visualized with a conceptual diagram, as depicted below
Cheaper but functionally
superior products will
generally be preferred
by customers
Customers detect
products rapidly.
Cheaper but functionally
superior products will
generally be preferred
by customers
Customers detect
products rapidly.
So , if prio r
retail e xpe rienc e
The new product
replaces the
functio na lities of
existing products at a
lower pric e
The new product will
be a successful
market entrant.
So , if prio r
retail e xpe rienc e
The new product
replaces the
functio na lities of
existing products at a
lower pric e
The new product will
be a successful
market entrant.
Reb uttal
The new
of the product are not
vital to customers.
Reb uttal
The new
of the product are not
vital to customers.
Reb uttal
The new
of the product are not
vital to customers.
Figure 7: The Toulmin chart as an example of a knowledge-intensive conceptual diagram
Visual Metaphors: Relating Domains to Improve Understanding
Card et al. (1999) state in their research anthology on information visualization that the
key research problem in the area of visualization is to discover expressive and effective
visual metaphors mapping abstract data to visual forms. A metaphor, according to the
Oxford Dictionary of Current English, is an example of the use of words to indicate
something different from the literal meaning. Metaphors rely on analogies between the
qualities of a sign and the comparable attributes of what is signified. The term
‘metaphor’ is derived from the Greek verb metapherein whose meaning can be
translated as „carrying something somewhere else“. A metaphor provides the path from
the understanding of something familiar to something new by carrying elements of
understanding from the mastered subject to a new domain. This is why Aristotle calls
the metaphor a tool of cognition. According to Aristotle, a metaphor provides rapid
information and is to the highest degree instructive; it facilitates the process of learning
(see also Eco 1984, p. 100 for this point). All of these aspects can be fruitfully used in
knowledge communication where visual metaphors offer effective and simple templates
to convey complex insights. Sparrow (1998, p. 71) stresses this point in the following
quote (my italics):
“A variety of representations can be used as visual analogies/metaphors. Here certain
properties of concepts are highlighted by juxtaposing the concepts in a way that
parallels a particular well-known relationship between concepts from another context.
So, for example, two sets of concepts may be depicted as on either side of a ‘balance’,
or set of scales.”
Visual metaphors can either be natural objects or phenomena (such as mountains,
icebergs, trees, or islands) or artificial, man-made artifacts (such as a house or a temple,
a funnel, a chain, or a ladder). Their main feature is that they organize information
meaningfully. In doing so, they fulfill a dual function (Eppler, 2003b): first, they
position information graphically to organize and structure it. Second, they convey an
insight about the represented information through the key characteristics of the
metaphor that is employed. As Worren et al. (2002, p. 1230) have pointed out, one
should also not neglect their mnemonic (i.e., facilitating remembering) and cognitive
coordination function (i.e., providing an area of mutual and explicit focus). Visual
metaphors can be grouped into four generic groups based on their root domain:
1. Metaphors based on natural phenomena (mountain, iceberg, tree, abyss,
diamond, tornado, waterfall, volcano, river, cave, etc.)
2. Metaphors based on man-made objects (balance, ladder, wheel, road, temple,
bridge, funnel, umbrella, bucket, pendulum, lever, radar, Trojan horse, etc.)
3. Metaphors based on activities (climbing, fixing, walking, reaching, driving,
eating, fishing, hunting, harvesting, juggling, pouring, fencing, etc.)
4. Metaphors based on abstract concepts (war, family, peace, law, chaos, fractal,
The two examples below are of the second group. We use the image of a bridge (Figure
7) to convey how to lead successful negotiations (loosely based on the Harvard
negotiation method, see the footnote below) and the picture (taken from the medieval
philosopher Ramon Lullus) of stairs leading to a fortress in order to illustrate the
necessary steps that lead to market innovations.
Position A Position B
Fix Parameters
Decision Criteria
Choose Option
Screen Options
Evaluate Options
Figure 8: The Negotiation Bridge: A visual metaphor that outlines a negotiation method2
2 BATNA in this context is an abbreviation for Best Alternative to a Negotiated Agreement. See: Lewicki, R.J.,
Saunders, D. M. & Minton, J. W. (1997). Essentials of Negotiation. Boston: Irwin Mc Graw-Hill or: Fisher, R. &
Ury, W. (1981) Getting To Yes. Boston: Houghton Mifflin Company.
Steps to Innovation
Innovation Strategy
New Market
Barriers of Entry
Competitor Moves
Business Angels
Expert Input
Burn Rate
Growth Potential
Figure 9: The Market Stairs that lead to successful market entries
In the second metaphor the innovation process is represented as stair steps , the market
to be captured as a town protected by city walls and the customer needs as the guiding
sun light.
One can extend this use of indirect communication to instil knowledge in others by
activating their interpretation effort beyond the domain of metaphors. Other visual
tropes can be employed to knowledge communication, such as visual irony, allegory
(visual story telling), visual paradoxes (e.g., graphic koans), or visual simile and
The concept of visual metaphors can hence be summarized as graphic depictions of
seemingly unrelated graphic shapes (from other than the discussed domain area) that are
used to convey an abstract idea by relating it to a concrete phenomenon.
Knowledge Animations: Dynamic and Interactive Visualizations
Knowledge Animations are computer-supported interactive visualizations that allow
users to control, interact and manipulate different types of information in a way that
fosters knowledge creation and transfer. Figure 7 illustrates an interactive, three
dimensional interface that visualizes the data of the New York Stock Exchange. It is a
dynamic visualization for managers who are used to supervise and control the New
York Stock Exchange. By interacting with the information, new insights are created.
When the user combines information, reduces or aggregates it, newly assembles it, or
views it from different perspectives he acquires knowledge about the represented
processes that goes beyond the stored data.
Figure 10: An Interactive Visualization helps to supervise the New York Stock Exchange. 3
Novel animated visual metaphors as the Infoticle metaphor (Vande Moere et al., 2004)
allow new, instructive ways to interact with information. In contrast to static
visualizations or applications in the field of information visualization, where users
interact with data, in the infoticle application data-driven particles (= Infoticles) help to
explore large time-varying datasets with reoccurring data objects that alter in time.
Animating these infoticles leads to a knowledge animation which allows seeing the
behaviour of individual data entries or the global context of the whole dataset.
In similar ways, the interactive parameter ruler, depicted in Figure 11, enables teams
and individuals to explore alternatives in real-time through the mobile and versatile
sliders in the ruler application. By moving the horizontal sliders from left to right, users
can change their ratings, their options or their agreement with certain parameters (e.g.,
of a product configuration, a client rating, a course assessment). By moving the sliders
vertically, they can bring the criteria listed in the left row into a new order (reflecting
the importance of each criterion, see Eppler, 2004).
3 Retrieved August 4, 2003,
Figure 11: The interactive parameter ruler enables teams to explore alternate in real-time
The above contributions illustrate that Knowledge Animations help to fascinate and
focus people, to enable interactive collaboration and persistent conversations, and to
illustrate, explore and discuss complex data in various contexts.
Knowledge Maps: Navigating and Structuring Expertise
Knowledge maps (Eppler, 2002) are graphic formats that follow cartographic
conventions to reference relevant knowledge. A knowledge map generally consists of
two parts: a ground layer which represents the context for the mapping, and the
individual elements that are mapped within this context. The ground layer typically
consists of the mutual context that all employees can understand and relate to. Such a
context might be the visualized business model of a company (e.g., the lending business
model of a bank), the actual product (e.g., a vehicle model in the case of a truck
company), the competency areas of a company (as in the example of the multimedia
company in section three), the value chain of a firm (as in the example of the market
research group below), or a simple geographic map. The elements which are mapped
onto such a shared context range from experts, project teams, or communities of
practice to more explicit and codified forms of knowledge such as white papers or
articles, patents, lessons learned (e.g., after action reviews or project debriefings),
events (i.e., meeting protocols), databases or similar applications, such as expert
systems or simulations. Knowledge maps group these elements to show their
relationships, locations, and qualities. In this paper, we refer to knowledge maps as
graphic directories of knowledge-sources (i.e., experts), -assets (i.e., core
competencies), -structures (i.e., skill domains), -applications (i.e. specific contexts in
which knowledge has to be applied, such as a process), or -development stages (phases
of knowledge development or learning paths). Some maps may utilize the conventions
of geographic maps, while others (such as the example below) employ symbols from
underground maps, parks or public garden maps, fictional (e.g. treasure island) maps,
Example: A quality development process needed to be established in an organization.
Traditional project plans, flyers and mails did not manage to get the attention, present
an overview and details and motivate the employees to act. A customized tube map
visualization4 (Figure 9 and 10) was introduced as ground layer to illustrates the whole
Figure 12: The tube map visualization (1,2 x 2,4 meter) presents an overview and details on a project. Each line
represents one target group, each station a project milestone. Each line (target group) stops at the stations (milestones)
where the target groups are involved. The stations are tagged with descriptions, dates or instructions.
4 Copyright by vasp datatecture GmbH,
Figure 13: A zoom-in presents an individual and a collective milestone (where different lines pass through).
As individual elements different subway-lines and -stations were used: Each subway-
line represents a target group, each station a milestone. The visualization was printed as
a poster (2,4 x 1,2 meters) and located at prominent locations in the organization. The
evaluation (Burkhard and Meier, 2004) has shown that the tube map visualization is a
powerful metaphor to communicate a project to different target groups and to build up a
mutual story. The employees considered it useful, because it provides an overview and
detailed information in one knowledge map. A similar metro structure was used to
document an already completed project and link the various results of the project
visually to one another. This interactive map (see below) depicts four years of project
events and documentation (taken from Eppler, 2003).
Outside Experts
Legacy Apps.
Dead Links
Grey Literature
Project Map
Team Knowledge
KM Case Book
Project Underground
Key Account
On Track 3
BSC K-Mapper
KM Glossary
Knowledge Brands
Active Plan
M. Franz
Analys is
B. Huber
MCM Zentral
O. Christ
S. Seufert
G. Nittbauer
R. Will
Figure 14: A Metro Map used for Project Knowledge Documentation
In this metro map each line designates a knowledge source, such as experts, documents,
software applications, websites, databases, or publications. The project’s time line is
visualized as a river running through the city from the top left hand corner towards the
right side.
Scientific Charts: Visualizing Domain Knowledge and Intellectual Structures
Knowledge Domain Visualization: Visualizing intellectual structures and mapping
scientific frontiers has been investigated by scholars from different perspectives and
times. Chen presents an excellent overview on this early knowledge visualization
domain. (Chen, 2003). Achievements in information visualization and in studying
scientific literature were the foundation for a new knowledge visualization direction,
which is called knowledge domain visualization or visual co-citation analysis. This
research focus investigates new ways of accessing scientific literature in digital libraries
(Chen, 1998; Chen, 1999b; Chen, 2000) by visualizing linkage and relationships
between scientific literature. Lin et al. (White et al., 2000; Lin et al., 2001; Lin et al.,
2003) research in the field of associative search visualization and co-citation author
maps. Based on computational algorithms interactive maps are automatically created
and present query relevant terms and relationships. In contrast to Chen these
visualizations do not illustrate the total system but the immediate environment that is
related to the query.
Visual Interfaces for the Exploration of Digital Libraries: With an increasing number of
digital documents new information retrieval paradigms become decisive. The need for
improved search result visualizations is described in an empirical evaluation of an
information retrieval system (Sutcliffe et al., 2000). Traditional text-based retrieval
systems are effective for specific searches, but for exploratory tasks users need new and
more effective approaches. A new field with it's root in information visualization
applies existing or new visualization methods to digital libraries to offer new methods to
better exploit existing information repositories. The field examines how our perceptual
processes as visual pattern recognition can be used to explore document spaces. Börner
and Chen (Börner and Chen, 2002) present an overview on this field. As integral parts
of such systems new query-result visualizations are important. The need for improved
search result visualization is described in different studies (Kleiboemer et al., 1996;
Chen et al., 1998; Sebrechts et al., 1999). Various systems that address these issues and
that bring together the advantages of information visualizations and information
retrieval are promising. Nowell et al. (1996) presents an overview of such systems, for
instance: Envision (Fox et al., 1993; Fox et al., 2002), a digital library of computer
science literature that provides an interface with several search result visualizations,
Gridvis (Weiss-Lijn et al., 2001), which provides manually produced metadata for each
paragraph or section level of a document, Roberts et al. (2002), which present a multiple
view system with search result visualizations. Another, related type of scientific
knowledge visualizations are visual literature review diagrams, as the Venn diagram on
information overload research depicted below. They are not automatically produced, but
designed by a reviewer. The example below illustrates the low degree of
interdisciplinary research regarding a research topic (in this case information overload).
Information Systems
Schick et
al., 1990
Iselin, 1988, 1993
Meyer, 1998
Schneider, 1987
Jacoby, 1984
Cook, 1993
Edmunds &
Morris, 2000
Grise &
Sparrow, 1999
Bawden et
al., 1992
Speier et al., 1999
Herbig &
Kramer, 1994
Swain & Haka, 2000
Snowball, 1980
Nelson, 2001
Koniger &
Janowitz, 1995
Simpson &
Prusak, 1995
Casey, 1980
Revsine, 1970
Chewning & Harrell, 1990
Abdel-khalik, 1973
Simnet, 1996
Wilkie, 1974
Scammon, 1977
Keller & Staelin, 1987
Malhotra et al., 1984
Muller, 1984
Owen, 1992
Meyer & Johnson, 1989
Galbraith, 1974
Griffeth, 1988
O‘Reilly, 1980
Tushman &
Nadler, 1978
Hiltz & Turoff, 1985
Tuttle & Burton, 1999
Hanka & Fuka, 2000
Rudd & Rudd, 1986
Berghel, 1997
Payne, 1976
Denning, 1982
Schultze & Van-
denbosch, 1998
Figure 15: A visual literature review diagram on information overload
5. Conclusion and Outlook
In concluding, we summarize the strengths and disadvantages of knowledge
visualization in their various fields of application. In terms of advantages, knowledge
visualizations offer cognitive, social, and emotional benefits. We synthesize these
strengths in the CARMEN acronym:
C oordination: They help to coordinate the communication of knowledge workers.
(Social benefit)
A ttention: They raise awareness and provide focus for knowledge creation and
transfer. (Cognitive benefit)
R ecall: They improve memorability and thus foster the application of new
knowledge. (Cognitive benefit)
M otivation: They energize viewers to engage in interpretation and explore the
graphic. (Emotional benefit)
E laboration: The process of visualizing knowledge leads to further understanding
and appreciation of concepts and ideas as one interacts with them.
(Cognitive benefit)
N ew insights: Knowledge visualizations can reveal previously hidden connections
and lead to sudden insights, a-ha experiences. (Cognitive Benefit)
As far as the limitations are concerned, there is evidence that visualization can have
drawbacks with regard to specific contexts. One should thus not neglect the risks
inherent in using such forms of visualization, namely the difficult maintenance of the
diagrams and maps, the reification of (at times) invalid views, and hence the possible
manipulation of users, or the possible distortion of reality through misinterpretations.
Future research will have to investigate these potential negative effects empirically in
authentic application contexts (see for example Blackwell and Green, 1999, for such a
Future Trends and Developments
In this section, we outline areas that we believe to be crucial for the success of the
knowledge visualization field. We highlight unresolved issues and future research
A Model for Knowledge Visualization
As this article makes clear, different isolated research areas are investigating the
potential of visualizations for the transfer and the creation of knowledge. While
different focus areas and interdisciplinary approaches exist, we believe that a
comprehensive framework that focuses on knowledge-intensive visualization is needed.
Such a framework should also outline (based on previous evaluation research) which
types of knowledge (such as know-why or know-how) are best represented in which
visualization format (such as diagrams or metaphors) and which purposes (such as
learning or knowledge creation). The knowledge visualization framework we have
presented serves as a first step in this direction. Future frameworks must also highlight
how complementary visualization, that is to say combinations of these formats, can be
fruitfully used.
Knowledge Visualization as Mediator between Strategic Management, Advertising
and Marketing
Today in organizations budgets for professional visualizers are allocated mainly for the
fields of advertising or corporate identity. Advertising has expertise to get the attention,
to address emotions and to illustrate a product/service. Corporate identity has expertise
on the use of a visual language to support the creation of a corporate brand. Both fields
are important. But they do not exploit the potential of visualizations. When it comes to
the transfer and creation of knowledge, especially of business relevant, complex or
abstract knowledge, knowledge visualization is powerful, as we discussed in this article.
But often budget and time is allocated, when it is too late. In our praxis we have seen
that knowledge visualization helps in early stages of projects. In the fields of strategic
management, marketing, public relation and advertising imaginary visualizations or
mental images (i.e. stories or metaphors) are already used to envision and illustrate
strategies, mutual targets or values. Storytelling in management to create and
disseminate knowledge is fruitful and discussed for instance by Loebbert (2003). We
believe that these stories can be visualized and combined with other visualization
formats to trigger and accelerate the creation and dissemination of knowledge. We
believe that knowledge visualization can become the function of a mediator of the
essential knowledge between strategic management, advertising, marketing, public
relations and corporate communication, because it combines imaginary (i.e. stories),
physical and digital visualizations. We believe that time and budget for knowledge
visualization will be integrated in future initiatives where the transfer and creation of
knowledge is important.
Knowledge Ambienting: Moving Knowledge Visualization off the screen
The fact that imaginary visualizations (such as visual tales) are essential in knowledge
visualization makes it clear that visualization is already leaving the screen and entering
other realms. While visualizations to create and disseminate knowledge in organizations
were originally seen as static objects we can now identify two trends:
First, new displays, media and carrier of information are developed. Current user
interfaces display information as "painted bits" on rectangular screens. New approaches
with its roots in Ubiquitous Computing (Weiser, 1991) or Augmented Reality (Feiner et
al., 1993; Wellner et al., 1993) attempt to change the paradigm of "painted bits" into
"tangible bits" (Ishii and Ullmer, 1997). As a consequence, the richness of human
senses and skills can be used for a much richer multi-sensory experience of digital
information. Computer generated visualizations, that today are presented on screens or
video projections, will soon merge with virtual space. An example of this trend is the
blue-c5 project at the Federal Institute of Technology in Zurich. It presents a tele-
immersive space for 3D collaboration in virtual environments (Gross et al., 2003).
Second, visualizations are becoming more dynamic and interactive, as discussed in the
section on knowledge animation. Visualizations are therefore no longer simply static
objects for the transfer of knowledge in the classic sender-recipient model. They
establish an iterative, collaborative process where the visualization (and new
knowledge) is dynamically co-created.
These two trends - first, that visualization is leaving the 2D computer screen and second
that visualizations are becoming dynamic and interactive- will offer new opportunities
for the creation and transfer of knowledge in organizations.
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... Such an output in turn fosters understanding for both individuals and groups by augmenting cognition through visualization (Salas and Fiore 2004). For completeness, even though it is only incidental to this chapter, it is useful to recall how knowledge visualization is defined; Eppler and Burkhard (2004) defined knowledge visualization as "all graphic means that can be used to construct and convey complex insights". An important aspect of this definition is that it does not reduce visualization to computer-generated visuals but includes handwritten or hand-drawn notes, graphs, posters or templates filled with visual signs. ...
This chapter focuses on the communication dimensions related to data visualization for both decision-making and outreach purposes. It emphasises why visualization matters, what matters when work- ing with data visualization and how to make data visualization matter even more. It combines well-established good practices in the domain of data visualization with lessons learned from con- crete insights stemming from the author’s practice in the field. The iterative nature of the visualization process and the fact that multi- disciplinary teams working on data visualization add significant value to the process and the outcome are also underlined. The chap- ter concludes with the importance of using the power of data, infor- mation and knowledge visualization to mobilise attention to chal- lenge and transform society in a world where volatility, uncertainty, complexity and ambiguity are common currencies.
Aiming at the problem of cognitive mismatch between the knowledge structure of the ordinary elderly and medical professionals, health knowledge visualization is used to help the elderly understand vague medical information clearly. Based on the literature research of domestic senile diseases, a study of optimizing medical report of urine testing was conducted, and the information display interface before and after the redesign is compared. The new medical treatment can make the people of ordinary elderly know their physical conditions more intuitively and comprehensively by health knowledge visualization, like providing the cartoon diagram and the color expression in accordance with cognitive limitations of the elderly. The user interface of new medical report can also effectively provide the elderly immediate precautions according to their actual needs of health supervision. The goal is to improve the efficiency and quality of decision-sharing for the elderly and to improve the therapeutic experience.
Learning refers to the process where students obtain knowledge, skills, and attitude through interaction with teachers, peers, and instructional information. With the development of AI technology, it has been applied in many aspects of the learning process. For example, knowledge recommendation algorithm can recommend the most suitable learning resources for learners, and knowledge tracking model can monitor the learning status of learners.
The article sets out to present conclusions from the conducted research into semantic network visualization as a user interface applied for the purpose of searching for business information. The article describes the results of the research comprising literature analysis as well as the validation of prototypes developed in Protégé. Based on the analysis of publications covering the latest research, four areas of research into semantic network visualization were identified, i.e. the development of new software, the application of techniques, technologies, and solutions making it possible to perform various operations concerning the semantic network visualization view, the verification of the use of various graphic designations, as well as users’ validation of interactive semantic network visualization. For each area, the main directions of research are specified, along with an indication of publications that describe them. Furthermore, the article provides conclusions from the validation of prototypes of ontologies for the area of users’ (experts’) analysis of economic and financial indicators using semantic network visualization. The research in question allowed the identification of three potential problems related to the use of semantic network visualization as a visual interface in searching for economic information.
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Las Red Eléctrica Inteligente (REI), conocida también como Smart Grid (SG) surge como respuesta a la necesidad de modernizar la red eléctrica actual, transformando la forma de distribuir, comercializar y consumir la energía. La REI proporciona una comunicación bidireccional entre proveedores de servicio y los usuarios conectados a ella. La infraestructura de telecomunicaciones es considerada como “la segunda red”, la cual es tan crítica en la operación de la REI como los elementos primarios. Por lo tanto, la infraestructura de telecomunicaciones deber ser planeada, diseñada, implementada y administrada con el mismo cuidado que la infraestructura eléctrica. En México se necesitan determinar los futuros usos y requerimientos de las telecomunicaciones y evaluar si las tecnologías existentes cumplen con estos requerimientos. El presente capítulo tiene por objetivo evaluar las tecnologías existentes o emergentes a través de una revisión de la literatura para el uso potencial de cada una ellas en la REI. Así también, se describen algunos de los retos a los que se enfrenta este nivel de la red.
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It is a great challenge to search the Web for precise information. It requires a combination of an efficient system and human intelligence to discern needed information from the daunting large amount of information on the Web. In this article, we discuss the design of a visualization-based middleware for search engines. The middleware is based on platform- independent Java 2 Enterprise technology. It allows a combination of computational power and human visual processing power in the search process. Two modules of the middleware are implemented, AuthorLink and ConceptLink. Each provides a practical search interface to real-world search engines. AuthorLink creates interactive co-cited author maps in real time from ISI's Arts & Humanities Citation Index. ConceptLink generates instant "concept maps" for MEDLINE searches and provides an interactive drag-and-drop interface for the PUBMED search engine.
The Internet provides an exceptional testbed for developing algorithms that can improve browsing and searching large information spaces. Browsing and searching tasks are susceptible to problems of information overload and vocabulary differences. Much of the current research is aimed at the development and refinement of algorithms to improve browsing and searching by addressing these problems. Our research was focused on discovering whether two of the algorithms our research group has developed, a Kohonen algorithm category map for browsing, and an automatically generated concept space algorithm for searching, can help improve browsing and/or searching the Internet. Our results indicate that a Kohonen self-organizing map (SOM)-based algorithm can successfully categorize a large and eclectic Internet information space (the Entertainment subcategory of Yahoo!) into manageable sub-spaces that users can successfully navigate to locate a homepage of interest to them. The SOM algorithm worked best with browsing tasks that were very broad, and in which subjects skipped around between categories. Subjects especially liked the visual and graphical aspects of the map. Subjects who tried to do a directed search, and those that wanted to use the more familiar mental models (alphabetic or hierarchical organization) for browsing, found that the map did not work well. The results from the concept space experiment were especially encouraging. There were no significant differences among the precision measures for the set of documents identified by subject-suggested terms, thesaurus-suggested terms, and the combination of subject- and thesaurus-suggested terms. The recall measures indicated that the combination of subject- and thesaurus-suggested terms exhibited significantly better recall than subject-suggested terms alone. Furthermore, analysis of the homepages indicated that there was limited overlap between the homepages retrieved by the subject-suggested and thesaurus-suggested terms. Since the retrieved homepages for the most part were different, this suggests that a user can enhance a keyword-based search by using an automatically generated concept space. Subjects especially liked the level of control that they could exert over the search, and the fact that the terms suggested by the thesaurus were "real" (i.e., originating in the homepages) and therefore guaranteed to have retrieval success.
This book contains several hundred displays of complex data. Suggestions and examples are given for methods of enhancing dimensionality and density for portrayal of information. The first chapter outlines methods of moving away from single dimensional layout. Through the use of multi-dimensional images, greater clarity can be achieved and amount of information displayed increased. The following chapters cover the use of micro-macro design to illustrate detail, the layering of information and colour. The final chapter covers the display of space and time data. Examples used to illustrate the techniques covered include maps, the manuscripts of Galileo, timetables, dance movement notations, aerial photographs, electrocardiograms and computer visualisations. -R.C.Medler
Conference Paper
Today's digital libraries (DLs) are content rich, multimedia, multilingual collections that are distributed and accessed worldwide. Designing useful interfaces to access, understand, and manage this knowledge has become an active and challenging field of study. Visual interfaces to DLs aim to shift the user's mental load from slow reading to faster perceptual processes such as visual pattern recognition. They draw on progress in the new field of Information Visualization.The workshop in 2002 continues the theme started at JCDL 2001. In addition, the growth of the field warrants new perspectives on some of the issues we have addressed last year.