Showing posts with label Mind Maps. Show all posts
Showing posts with label Mind Maps. Show all posts

24 March 2025

🏷️Knowledge Representation: On Mind Maps (Quotes)

"A mind map harnesses the full range of cortical skills—word, image, number, logic, rhythm, color, and spatial awareness - in a single, uniquely powerful technique. In doing so, it gives you the freedom to roam the infinite expanse of your brain." (Tony Buzan, Barry Buzan, "The Mind Map Book: How to Use Radiant Thinking to Maximize Your Brain's Untapped Potential", 1996)

"Delay time, the time between causes and their impacts, can highly influence systems. Yet the concept of delayed effect is often missed in our impatient society, and when it is recognized, it’s almost always underestimated. Such oversight and devaluation can lead to poor decision making as well as poor problem solving, for decisions often have consequences that don’t show up until years later. Fortunately, mind mapping, fishbone diagrams, and creativity/brainstorming tools can be quite useful here." (Stephen G Haines, "The Manager's Pocket Guide to Strategic and Business Planning", 1998)

"An effective mind map is one that works for you and therefore it is your tailoring and your emphasis, images, colours, codes and style that will determine its effectiveness. Try to develop the habit of taking down all your notes in mind map format. If you are required to give presentations, do this from a mind map. When you are at meetings, take down the minutes in mind map layout and just notice the difference in your ability to retain exactly what happened at that meeting and compare it with your usual logical/analytical method of recording minutes." (Peter F Haddon, Mastering Personal and Interpersonal Skills, 1999)

"Mind mapping is a technique whereby information is summarised in a form of pictorial representation which depends very much on the creativity of the individual involved. The idea is that when information is pictured in colourful word associations backed up by sketches or even stick drawings of the key words, it is far more easily remembered, much like when looking at a photograph you can recall in detail the happenings that led up to and followed the incident." (Peter F Haddon, Mastering Personal and Interpersonal Skills, 1999)

"Knowledge maps are node-link representations in which ideas are located in nodes and connected to other related ideas through a series of labeled links. They differ from other similar representations such as mind maps, concept maps, and graphic organizers in the deliberate use of a common set of labeled links that connect ideas. Some links are domain specific (e.g., function is very useful for some topic domains...) whereas other links (e.g., part) are more broadly used. Links have arrowheads to indicate the direction of the relationship between ideas." (Angela M O’Donnell et al, "Knowledge Maps as Scaffolds for Cognitive Processing", Educational Psychology Review Vol. 14 (1), 2002) 

"Mind Mapping uses the full range of the brain's abilities, placing an image in the center of the page in order to facilitate memorization and the creative generation of ideas, and subsequently branches out in associative networks that mirror externally the brain's internal structures. By using this approach, the preparation of speeches can be reduced in time from days to minutes; problems can be solved both more comprehensively and more rapidly; memory can be improved from absent to perfect; and creative thinkers can generate a limitless number of ideas rather than a truncated list." Marshall Goldsmith et al, "The Many Facets of Leadership", 2002)

"[a mind map is a] "visual note-taking process that includes key words and pictures illustrating the relationships among concepts." (Ruth Colvin Clark, Chopeta Lyons, "Graphics for Learning: Proven guidelines for planning, designing, and evaluating visuals in training materials" 2nd ed., 2011)

"Data visualizations can also play a critical role when it is time to disseminate and communicate evaluation findings. Data visualization engages and supports program stakeholders by increasing their capacity to understand data and participate in the evaluation process. Collaboratively developed mind maps, logic models, and graphic illustrations can facilitate understanding of the findings and their implications by depicting a program’s most important activities, outcomes, and ultimate goal in a concise and clear manner. Well-designed interactive visualizations for reporting and community engagement help stakeholders answer questions of import within context and place engaged stakeholders in the driver’s seat in terms of defining variables and interpreting results." (Tarek Azzam et al, "Data Visualization and Evaluation", "Data visualization, part 1: New Directions for Evaluation", 139], 2013)

"Paradoxically one of the greatest advantages of mind maps is that they are seldom needed again. The very act of constructing a map is itself so effective in fixing ideas in memory that very often a whole map can recalled without going back to it at all. A mind map is so strongly visual and uses so many of the natural functions of memory that frequently it can be simply read off in the mind's eye." (Peter Russell, "The Brain Book: Know Your Own Mind and How to Use it", 2013)

"With the adoption of a more schematic and abstract construct, deprived of realistic arboreal features, a tree diagram could sometimes be rotated along its axis and depicted horizontally, with its ranks arranged most frequently from left to right. Horizontal trees probably emerged as an alternative to vertical trees to address spatial constraints and layout requirements, but they also provide unique advantages. The nesting arrangement of horizontal trees resembles the grammatical construct of a sentence, echoing a natural reading pattern that anyone can relate to. This alternative scheme was often deployed on facing pages of a manuscript, with the root of the tree at the very center, creating a type of mirroring effect that is still found in many digital and interactive executions. Horizontal trees have proved highly efficient for archetypal models such as classification trees, flow charts, mind maps, dendrograms, and, notably, in the display of files on several software applications and operating systems." (Manuel Lima, "The Book of Trees: Visualizing Branches of Knowledge", 2014)

"Essentially, a mind map is a type of node-link diagram in which the nodes represent concepts and the links represent relationships between concepts. The central idea to be explored is placed in the middle of the page and it is expanded out from there. Usually mind maps are drawn as tree structures with no cross links between branches, but this can be restrictive." (Colin Ware, "Information Visualization: Perception for Design" 4th Ed., 2021)

"The educational use of mind maps and concept maps would seem to fit well with constructivist theory. To construct such a map, students must actively draw out links between various concepts as they understand them. The problem is that the cognitive engagement tends to be somewhat superficial for mind maps, since it does not require that students think deeply about the nature of the links." (Colin Ware, "Information Visualization: Perception for Design" 4th Ed., 2021)

"Idea mapping offers the power to represent qualitative data, describe relationships, and enable one to see the 'big picture'. Further, mapping allows us to represent data in a way that facilitates the conceptualizing of its meaning. It provides a 'map', which makes it possible to observe macrophenomena, discover trends, and generate creative options. Idea mapping makes it possible to represent multiple dimensions of a situation without losing sight of any of its parts; it is an efficient way to manage an overwhelming amount of qualitative information. Finally, it offers a way to present information to clients in a graphic form that is both easy to understand and data rich. Often, an entire strategic plan can be represented in one map." (Terry Moore)

23 July 2011

🕸️Web x.0: Search Queries Tools ( Part I: Web Seer)

Since quite some time, Google provides an autocomplete feature extended to combinations of words. That’s quite an useful feature because often it “saved” my time from typing full words or combinations of words. What’s interesting is that the autocomplete algorithm provides the terms based on user’s search activities. I was asking myself if we could do more with search queries. This evening, while browsing, I discovered A. Smarty’s post on “How To Visualize and Play with Google Suggest Results”, in which she shortly presents three interesting tools: Web Seer, What do you suggest and Soovle. As I found out there are several other tools like Übersuggest, Quintura, etc. In this post I will focus only on Web Seer, following to review shortly several other similar tools in the next posts.

Web Seer allows users to compare the “matches” between two Google queries, for example “are man” vs. “are women”, “will he” vs. “will she”. To remain in blog’s thematic , I checked tool’s output for “data” vs. “information” and “information” vs. “knowledge”:

search comparison - data vs information

The query results for both terms are somehow predictable – “data mining”, “data warehouses”, “data entry” and “data values”, respectively “information architecture”, “information management”, “information security”, “information technology”, “information is beautiful” (see also the book) are quite popular terms in the scientific and non-scientific literature.  I would expect the comparison is based on the most popular terms, because the two concepts don’t share many common terms, and even if there are some common terms within the above results (e.g. “data architecture”, “data systems”) they aren’t highly ranked. Arrows’ weight depicts the number of occurrences of the respective terms, which combined with the terms themselves, help to make an idea of the strength and resemblance existing between two concepts.

Climbing the DIKW scale here are the comparisons between information and “knowledge”, respectively “knowledge” and “wisdom”:

search comparison - information vs knowledge

search comparison - knowledge vs wisdom

As it seems the results are consistent between relations, same combinations being used in two comparisons in which the same term is involved, life in the above diagrams. It’s natural that the results are also commutative, in order words “knowledge” vs. “information” renders same result as “information” vs. “knowledge”.

search comparison - knowledge vs information

The association is also reflexive:

search comparison - information vs information

And transitive, as “data” vs. “information”, and “information” vs. “knowledge” lead to “data” vs. “knowledge”:

search comparison - data vs knowledge

The algebraical operations are not so important, though some consistency of the results is needed between representations. It’s interesting that the comparison is influenced by a space placed at the beginning (e.g. “ data”) or end (“data ”), as can be seen in the following representation of the two:

search comparison - data vs data

 I would expect other similar signs (e.g. punctuation signs, special characters) influence the comparisons too. Talking about DIKW, the knowledge pyramid, let’s see the comparison between “DIKW” and “data information knowledge wisdom”:

search comparison - DIKW   

As the two concepts have close semantics, “DIKW” is the acronym for “data information knowledge wisdom”, here’s the comparison between two synonyms: “distribution” vs. “diffusion” (like in distribution/diffusion of knowledge). As can be seen the association is stronger.

search comparison - distribution vs diffusion

Actually the first attempt with the tool was a comparison “concept map” vs. “mind map”:


search comparison - concept vs mind map
Which looks slightly different than “concept maps” vs. “mind maps” (so the plural form of words introduces variances):

search comparison - concept vs mind maps

 Considering the few examples run, the tool is quite intuitive and catchy. I would consider its utility as relative, even if the above examples are not representative and the relationships between them are more contextual.  Still it’s a good tool for identifying automatically the relations/associations between concepts, to identify associations’ strength and maybe several semantic connotations.  It would be interesting to see only the common terms, as many K-maps focus on this aspects, to introduce language and context, and the possibility to compare more than two terms (for example using Venn diagrams) or to show more/less common terms.

30 April 2011

🔖Knowledge Representation: Weighted Categorization of 4 Knowledge Maps

Last year I was brainstorming with M. Mahmoud on a weighted categorization of various Knowledge Maps (K-Maps), this as input for his Diploma paper. He focused only on Mind Maps, Conceptual Graphs and Concept Maps, considered by us as mature enough and rich in expressing the various facets of knowledge. We came up with the following tables which could be used to evaluate the value of each K-Map based on a mixture of criteria provided by [1] and [2], considered also in [3]: content type, recipient type, content formats, layout, creation mode, purpose, knowledge type, visualization type, graphical form and function type. Each criteria is weighted on a 1..5 scale, from weak to strong. 

A downside of the below tables is that they were based on our understanding of the respective K-maps and were not based on survey or any other types of scholastic techniques, so the values should be used with caution. We also attempted to evaluate the K-maps from the perspective of humans (H) and computers (C), as the two type of M-map consumers come with different requirements. We find out that the evaluation of K-maps from this perspective isn’t so easy to achieve, not being aware of the all results in the field, the lack of time being other important consideration. 

We managed to apply this distinction only to content type, where a value of 2H refers to human, while a value of 2C refers to computers. The values considered for the other categories are considered only from the perspective of humans. What is missing from these tables, in respect to the initial tables, are the comments made to the some of the below evaluations.

Content Type

Mind Maps

Conceptual Graphs Ontology Concept Maps Weight Notes
Methods  2H, 2C 2H, 2C 4-5H, 2-3C 3H, 3C 2  
Processes 2H, 2C 1H. 1C 4H, 3C 4H, 3C 3  
Experts 5H, 2C 3H, 3C 5H, 2C  5H, 2C 3  
Organizational subdivision 5H, 1C 1H, 3C  5H , 1C 5H, 1C 2  
Lessons Learned and experiences  4H,1C 3H, 4C 3-4H, 1C  5H, 2C 3  
Skill and Competencies 5H, 2C 3H, 2C 5H, 2C  5H, 2C 3  
Concepts 4-5H, 3C 3-4H, 3C 5H, 3C 5H, 3C 5  
Events 4-5H, 3C 3-4H, 3C 5H, 3C 5H, 3C 4 same as concepts
Patents 2H, 1C 2H, 1C 3H, 1C 3H, 1C 2  
Communication flow 4-5H, 4C 4-5H, 4C 4-5H, 4C 4-5H, 4C 5  
Interest or Knowledge needs 4-5H, 2C 4-5H, 2C 4-5H, 2C 4-5H, 2C 4  
Recipient Type Mind Maps Conceptual Graphs Ontology  Concept Maps Weight Notes
Individual  5 4 4 5 5  
Team 5 4 5 5 3  
Organization And Networks 5 3 5 5 3  
Dyadic 5 4 4 5 4 same as individual
Departmental  5 4 5 5 2 same as team
Community  5 3 5 5 3 same as organization
Inter-Organization Maps 5 3 5 5 2 same as organization
Content Formats Mind Maps  Conceptual Graphs Ontology Concept Maps Weight Notes
Websites 5 3 4 5 3  
Documents  4 4 4 4 4  
DataBases or Repositories 5 5 5 5 5  
Learning Object  5 3 5 5 2  
Online Courses  3 1 3 3 3  
Notes taking 4 1 2 4 4  
Layout Mind Maps Conceptual Graphs Ontology  Concept Maps Weight Notes
Chained 3 4 5 5 3  
Clustered 5 2 5 5 4  
Hierarchical  5 3 5 5 4  
Radial 5 1 4 5 3  
Networked 5 3 5 5 5  
Creation Mode Mind Maps Conceptual Graphs Ontology Concept Maps Weight Notes
Manual 5 3 4 5 5  
Automated 2 3 2 2 3  
Semiautomated 3 4 3 3 3  
Collaborative 5 4 5 5 4  
             
Purpose Of KM Process Mind Maps Conceptual Graphs Ontology Concept Maps Weight Notes
Knowledge Creation 5 2 4 5 3  
Knowledge Assessment  or audit 5 3 4 5 4  
Knowledge Identification 5 4 5 5 4  
Knowledge Development or Acquisition 5 3 4 5 4  
Knowledge Transfer 5 2 4 5 5  
Sharing Or Communication 5 2 4 5 5  
Knowledge Application 4 3 5 4 3  
Knowledge Marketing  Maps 4 2 2 3 2  
Knowledge Type Mind Maps Conceptual Graphs Ontology Concept Maps Weight Notes
Know-What 5 4 5 5 5  
Know-Who 5 4 5 5 5  
Know-Why 5 4 5 5 5  
Know-Where 5 4 5 5 5  
Know-Who 5 4 5 5 5  
Visualization type Mind Maps Conceptual Graphs Ontology  Concept Maps Weight Notes
Sketch 3 1 2 4 4  
Diagram  3 1 2 3 3  
Image 3 1 2 3 2  
Map 2 1 2 2 2  
Object 1 1 2 2 3  
Interactive Visualization 3 1 3 3 4  
Story 3 2 2 3 4  
Graphical Form Mind Maps Conceptual Graphs Ontology  Concept Maps Weight Notes
Table 2 2 3 3 3  
Base Map     3 3 3 3 3  
Diagrammatic Maps 3 3 3 3 3  
Cartographic Maps  3 1 4 3 2  
Geographic Maps 3 1 4 3 2  
Heuristic Maps 3 1 3 3 3  
Metamorphic Maps 2 2 3 3 4  
Interactive Maps  4 2 3 4 4  
Mental Maps 4 2 2 3 4  
3D Maps 4 1 2 2 4  
Function Type Mind Maps Conceptual Graphs Ontology  Concept Maps Weight Notes
Coordination  3 2 4 4 5  
Attention  3 1 2 3 4  
Recall 4 1 2 4 5  
Motivation 3 1 2 4 3  
Elaboration  4 2 4 4 5  
New Insight 4 3 4 4 5  


[1] Burkhard, R., Meier, M., Smis, M., Allemang, J., Honisch, L. (2005). Beyond Excel and Powerpoint: Knowledge Maps for the Transfer and Creation of Knowledge in Organizations. Proceedings of the 9th International Conference on Information Visualisation, p76-81. [Online] Available from: http://ieeexplore.ieee.org/Xplore/login.jsp?url=/ielx5/10086/32319/01509062.pdf?arnumber=1509062  (Accessed: 13 March 2009)
[2] Eppler, M.J. (2008). A Process-Based Classification of Knowledge Maps and Application Examples In: Knowledge and Process Management, Vol15, No1, p59–71. John Wiley & Sons. [Online] Available from: www.interscience.wiley.com (Accessed: 13 March 2009)
[3] Nastase, A. (2009). Utilizing Mind Maps as a Structure for Mining the Semantic Web. [Online] Available from:  http://www.scribd.com/doc/16612282/Dissertation-paper-Utilizing-Mind-Maps-as-a-Structure-for-Mining-the-Semantic-Web (Accessed: 20 June 2009)
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