🔖Knowledge Representation: 3D Maps using 3D TopicScape Pro

Today I took the time and played with 3D TopicScape Pro, a nice 3D tool that can be used for Mind and Concept Mapping. 3D TopicScape Pro together with 3D TopicScape Lite, products of TopicScape, can be run on Windows 2000, XP and Vista.

From a first user experience the look and feel is interesting, the tool is easy to use and has a small learning curve, and it includes a few demos which make the learning process easier. The Landscaped Maps seems to be useful for representing several levels of children, though when the number of children is greater than 4-5, the labels are hard to see. The topics are represented as cones (see Figure 1) and to each topic can be attached up to 10 tags, the flagging of topics as important allows easier visual identification.

Figure 1: Learning Mind Map created with 3D TopicScape Pro

The tool offers multiple views – home, full, top, tag pool and hit list, the later offering an historical list of Map’s elements (see Figure 2). It includes several skins, rich editing and configuring features that add a plus to overall usability.

Figure 2: Hit List for the above Mind Map

Frankly I was expecting more from the tool and personally I prefer the 2D version of the Map (see Figure 3), it is less graphically loaded, and the increase border or text size can be used to obtain the same visual contrast as in landscapes. 2D Maps have the advantage that they can be constructed using Visio or PowerPoint, two of the tools used by many IT professionals and managers.

In exchange, I would use a 3D Map for representing weighted Topics, in which cone's size would be proportional with its weight. Figure 3: Personal Topicscape, sample 2D Mind Map available with 3D TopicScape Pro

The site offers also a collection of more than 1000 Mind Maps which could give you a feeling what Mind Mapping is about. Actually from the TopicScape’s blog I found the link to a nice source for learning how to make a Mind Map.

Technology x.0: Finally the Digital Book Reader

Yesterday my Sony PRS-700BC Digital Book Reader finally came, after two weeks and a trip overseas. I wanted so much a Digital Book Reader that I bought one, even if it doesn’t justifies entirely the price though might be useful when reading late in the evening or in trips in which is not possible to pack more than 1-2 books, not to forget the multitude of electronic documents not available in hardcopy. I was thinking to wait for Amazon’s Kindle DX, presented to the public a few weeks ago, unfortunately its (un)availability on European market and nice price made me to give up the idea of buying one. In exchange Sony’s reader looked more attractive as price and what attracted me at PRS-700BC was its touch-screen display and the possibility to add annotations and highlight text, these capabilities not being available in previous models. Using the stylus or direct touching, PRS-700BC allows you to select a piece of text (within the same page), save it and make it available in Notes section. 

I tested it using PDF documents and Sony’s proprietary format BBeB (Broad Band eBook), it worked acceptable as long the document is adequately formatted, the selection functionality working awkwardly in some PDF documents. The good rendering of PDF documents depends on font’s size, its length and disposal within page; even if font size is changed, the text might not be uniformly rendered, mathematical formulas being deformed, the text loosing of content. In order to avoid text’s deformation the text can be zoomed though I find the feature a little cumbersome to use for continuous reading. There would be also the possibility of exporting other documents to BBeB format, not sure if it really makes sense to do that…

Why am I talking about Digital Readers in this blog?! First of all because PRS-700BC provides the capability of annotating, highlighting and extracting text from a document, much of what we try to do with Web Pages within Web 2.0 in the attempt to create metadata and a read-write Web. The Notes thus created allow navigating back to the document it contains and in theory can be further used to partially index the document, partially because it doesn’t allows jumping between all occurrences like the search functionality. At it seems the full-word search provides hints based on previous annotations and highlights, that’s a nice feature.

I wonder whether the previously selected text can be also extracted on a PC in a document together with other information about the document that contains it; normally it should be possible, it’s just a question of programming effort. The text thus obtained could be reused in documents’ indexing or in Knowledge Maps. I hope that future models will have the capability of creating Maps inside the Reader and will that provide richer text formatting and processing.

Unlike Sony’s Reader, Kindle DX offers wireless connectivity which allows browsing directly Web Pages; Sony should consider doing the same! Just imagine that you can annotate Web pages on your own Reader, isn’t that something?! Of course, it won’t work so easy with heavy content Web Pages, though that’s a start… Anyway, Digital Readers are in their baby steps, there is more to be expected from the use of digital paper technology on which such devices are based. Let’s see what the future reserves us in this direction!

🔖Knowledge Representation: Mind Maps

In 70’s Tony Buzan coined the term of Mind Map for his visual tool based on “radiant thinking” principle, cataloguing it as a “powerful graphing technique”, “expression of radiant thinking” or “a natural function of human mind”. He made public the concept in his first book on Mind Maps that appeared in 1974, “Use Your Head”, one year later appearing “The Mind Map Book”, over the years, if we give credit to [3], the number of books reached 85, being sold over 5 millions copies worldwide, in 100 countries and translated in 30 languages. Quite impressive, isn’t it?!

A Mind Map is centred on a single idea (in some sources referred as topic, subject, theme or question), other ideas being associated to it in a radial fashion, resulting in the end a Map of ideas, from here the alternative denomination of Idea Map. “Idea” is maybe a too general term because it can represent a thought, concept or a statement in which multiple concepts are used. In most of the Mind Maps met, ideas are expressed in the form of Key-Words, and sometimes of symbols or images, especially on digital Maps. A Key-Word is supposed to encapsulate “a multitude of meanings in as small a unit as possible” [1], thus ideas reach to be expressed as single words, each word being the label in a hierarchical network. Maybe an example will make some light, so supposing that “Happiness” is the central idea, we can associate to it words that we relate in our mind to Happiness: “family”, “good job”, “free time”, “money”, “love”, “vacation”, etc. Each of these ideas can be further extended with other associations, “family” could be associated for example with the name of “wife”/”wives”, “husband(s)”, “kid(s)”, “dog(s)”, “parents” and “grandparents”, “cat(s)” and any other pats we consider to be part of the family. A “good job” presumes “good remuneration”, “appreciation”, “good boss”, “nice colleagues”, “nice environment”, “potential”, etc. “Free time” could include all the activities somebody likes to have in his/her free time; same exercise can be done for each idea included in the Map, ideas can be associated over and over again with other ideas. It seems like a never ending story… when do we stop then? Most probably when the paper ends or we get bored, these are two possible answers too, in the end it’s up to each person, how detailed he wants the Map, what he/she wants to achieve, etc.

Happiness – Mind Map created with FreeMind

A Mind Map can be regarded as a tree, in which the trunk represents the topic, the labelled leaves represent ideas, the forked branches themselves supporting the whole structure of the tree, their multiple forking representing the degree of detail the Map holds. The comparison with a tree is not accidental, tree-like drawings has been used since Antiquity to encode meaning (e.g. Tree of Life, Tree of Love), moreover representational purpose can be given also to the roots of the tree for example to represent base or fundamental ideas on which the whole foundation is built. Unlike trees, it could happen for example that two ideas from different braches can be associated too, for example “money” with “good job” resulting cross links between ideas. With each cross-links added the structure of the Map changes, becoming more like a network, though still relying on previous radial structure which becomes the Map’s backbone. Network-like Maps are more natural to represent knowledge, as knowledge has a networked rather than hierarchical structure.

A Map can go through multiple stages, iterations if you want, some ideas are deleted, others added, new associations are made, techniques are improved, and so on. Therefore such Maps are evolutional, they can change over time as people identify new associations, acquire new information or knowledge, change their values, change themselves and even their way of thinking…Excepting the radial disposition of ideas there are theoretically no other constraints, people can use their imagination and built all kind of Maps. Moreover, people can use visual rhythm, patterns, colour or spatial awareness (dimension and gestalt) to make Mind Maps easier to read, understand or navigate. Somebody can use his artistic talent and make a kind of piece of art from a Map, with a little imagination and skill a 2D Map can become 3D. In T. Buzan’s books you can find lot of propaganda for the use of Mind Maps and the benefits of its various characteristics together with references to (important) studies concerning learning and brain/mind theories.

A Map generally can be created by multiple people, the addition of ideas can be done independently or through consensus, the collective work can start from an idea, an already existing Map or the augmentation of all involved people’s Maps. Such collective or collaborative Maps can be used for example in learning or brainstorming, consensus playing an important role, and for example in a digital Map can be seen how the Map itself evolved and eventually also how the consensus was reached. [2] considers that there are 250 million Mind Mappers all over the world, jumping over the basis used for this consideration, even if their number raises up to several millions, that’s quite a number. Many Mind Mappers buy rich-functionality software tools for drawing digital Mind Maps, others resume to less rich functionality but free tools, they integrated the technique in everyday life, learning, teaching, presentations, decision-making, etc. It’s a form of knowledge representation, though the creation of Mind Maps is mainly for personal use, even if many Maps are available already in the public domain.

On the other side researchers occupy their time by building more or less complete ontologies, above their other characteristics, they imply consensus and quite an effort and coordination. Why not take advantage of the impressive number of Mind/Knowledge Mappers, give them rich and free software tools, and allow them to make explicit their knowledge or map the knowledge available on the Web?! Is the idea plausible?! How many of you haven’t underlined words or phrases of interest in a book or article creating thus bookmarks?! How many of you tried to built a mental image (Map) of how they fit together or into the existing knowledge? If we consider the “success” of folksonomies, of Knowledge Maps themselves, the increasing number of Web Sites and blogs on this topic, I am strongly convinced that the transition from folksonomies to Maps will happen pretty soon, once the Web Technologies in particular and Web’s evolution in general will allow that.

References:
[1] Buzan, T. (1991). Speed Reading. Ed: 3rd Plume. ISBN: 978-0452266049
[2] Buzan, T., Buzan, B. (2007). The Mind Map Book. BBC ACTIVE. ISBN: 978-1-406-6102
[3] Buzan.com.au. (2008). Tony Buzan. [Online] Available from: http://www.buzan.com.au/buzan_centre/tony_buzan.html (Accessed: 29 May 2009)

🔖Knowledge Representation: Utilizing Mind Maps as a Structure for Mining the Semantic Web

In the past 2 and half years I followed the Online Masters Programme of University of Liverpool, it was intriguing, fun, time consuming and quite an effort as energy, money and personal life, and I hope it will pay back in time, the sooner the better. The modules were quite entertaining, I learn lot of new stuff and two years passed fast and slower than expected, then the dissertation came and things got pretty tough as I wanted to make it useful for me, to learn something meaningful on which I can built in the future and not something that will rot in a corner of the brain. I was not sure what to choose and frankly not even what I supposed to do.

During the last modules I had the chance to read some material on Tony Buzan’s Mind Maps and it looked intriguing, I wish I had have read that stuff long time ago, but in the end better later than never. Why I found Mind Maps intriguing? First because they allow taking notes in a radiant fashion rather than using the old fashioned linear approach, by starting with a single idea (also referred as concept, subject, question) and built around it a whole Map using associations. In Mind Maps Key-Words are used to encapsulate a variety of meaning in smallest possible units, this step allowing some information filtering and processing, “obligating” the brain to actually integrate the new information in existing knowledge and represent already existing knowledge, identifying missing links, triggering other questions, etc. Thus on a piece of paper or in a electronic document, somebody can represent how concepts in a read material link to each other, making the subject clearer and I think easier to memorize and recall. Mind Maps can be also used to give life to own mental representations, as we all have created, voluntarily or involuntarily and map of the world we live in. Secondly, Mind Maps use symbols and graphical images, visual rhythms and patterns, colour and spatial awareness (dimension and gestalt), allowing people to take advantage of a broader set of cortical skills.

Given these characteristics, Mind Maps seems to be perfect tools for Knowledge Representation in particular and Knowledge Management in general. During the Web Applications module I tangentially learned about XTM (eXtensible Topic Maps) and ontologies for Knowledge Representation, though ontologies call for experts and come with many issues, while XTM is a standard for Knowledge Interchange and targets internal representation in computers. On the other side digital Mind Maps are more flexible than ontologies, target a broader range of users, have the potential of harnessing the Collective Intelligence, one of Web 2.0’s competences, by allowing users to map their knowledge or the knowledge existing on the Web in documents. This is how appeared the title of my Dissertation paper, “Utilizing Mind Maps as a Structure for Mining the Semantic Web”.

While diving in the subject, I found out that Mind Maps are just one of the Knowledge Maps used for various tasks, a search trough the literature revealing about 50 terms used to designate various types of Maps: argument maps, brace maps , bridge maps, bubble maps, causal maps, circle maps, cluster maps, cluster vee diagrams, clustering, cognitive maps, concept circle diagrams, concept maps, conceptual graphs, congregate maps, diagnostic maps, double bubble maps, dynamic cognitive maps, ecological maps, extended fuzzy cognitive maps, flow maps, frames, fuzzy cognitive maps, fuzzy relational maps, group maps, historical maps, idea maps, knowledge maps, mental maps, mind maps, multi-flow maps, neural cognitive maps, neutrosophic cognitive maps, node-link mappings, ontology, oval maps, probability fuzzy cognitive maps, rule-based fuzzy cognitive maps, semantic maps, semantic nets, semantic networks, shared maps, social maps, social mess maps, spider maps, strategy maps, taxonomy, text graphs, thinking maps, tree maps and virtual maps. Actually, the list might be much bigger, I expect I left out by mistake several terms, while on others I haven’t came across them until now.

From several considerations, I preferred to treat the subject from the perspective of Knowledge Maps, so maybe a better title for my paper would have been “Utilizing Knowledge Maps as a Structure for Mining the Semantic Web”. As I found out later this cost me a huge amount of time and effort, I longed for more I could chew in the dedicated amount of time for a Dissertation paper, not having the time to bring the paper to the desired final form, letting out some research material and ideas. Anyway, now it’s over, good or bad the paper is finished and waiting for the final results. With this blog I’m hoping to bring into light some of the ideas I couldn’t put in the paper, help me do to further research into the subject and hopefully get also some feedback.

I’m not sure yet whether I can put the paper in the public domain, therefore here is paper’s Table of Contents, with the mention that some of the topics (e.g. Fuzzy Cognitive Map) have only an informative character.

1. The Web

1.1 Introduction

1.2 Web 2.0

1.3 The Semantic Web

1.4 Semantic Web Problems

1.5 Beyond the Semantic Web

1.5.1 The Noosphere

1.5.2 Cognitive Machines

2. Philosophical Grounds

2.1 Introduction

2.2 From Meaning to Concept

2.3 Syntax, Semantics and Pragmatics

2.4 From data to wisdom

2.5 Types of Knowledge

2.6 Connectivism

2.6.1 Introduction

2.6.2 Chaos

2.6.3 Network

2.6.4 Complexity

2.6.5 Self-organization

2.7 Intelligence and Collective Intelligence

2.7.1 Intelligence

2.7.2 Collective Intelligence

2.7.3 Collective Web Intelligence

2.7.4 Web Technologies and Collective Intelligence

2.7.5 Offline Collective Intelligence

2.7.6. Collective Intelligence Forms

3. Knowledge Management

3.1 Introduction

3.2 Knowledge Representation

3.2.1 Introduction

3.2.2 Sub-conceptual level

3.2.3 Symbolic level

3.2.3.1 Generalities

3.2.3.2 The Frame Problem

3.2.3.4 The Symbol Grounding Problem

3.2.4 Conceptual level

3.2.5 Associationist level

3.2.6 Semantic level

3.3 From Mental Models to Knowledge Representation Structures

3.4 Historical Overview

3.5 Vocabularies

3.5.1 Controlled Vocabularies

3.5.1.1 Introduction

3.5.1.2 Indexing Schemes

3.5.1.3 Classification schemes

3.5.1.4 Thesauri

3.5.1.5 Taxonomies

3.5.2 Uncontrolled Vocabularies

3.5.2.1 Introduction

3.5.2.2 Folksonomies

3.6 Maps

3.6.1 Introduction

3.6.2 Semantic Nets

3.6.3 Frames

3.6.4 Mind Maps

3.6.5 Conceptual Graphs

3.6.6 Concept Maps

3.6.7 Neural Networks

3.6.8 Cognitive Maps

3.6.9 Fuzzy Cognitive Maps

3.6.9.1 Fuzzy Cognitive Maps

3.6.9.2 Rule-Based Fuzzy Cognitive Maps

3.6.9.3 Extended Fuzzy Cognitive Maps

3.6.9.4 Dynamic Cognitive Networks

3.6.9.5 Neural Cognitive Map

3.6.9.6 Neutrosophic Cognitive Maps

3.6.9.7 Probability Fuzzy Cognitive Maps

3.6.9.8 Fuzzy Relational Maps

3.6.10 Knowledge Maps

3.6.11 Topic Maps

3.6.12 Ontologies

3.6.12.1 Ontologies

3.6.12.2 Ontology Engineering

3.6.13 Other Knowledge Representation Structures

3.7 Analyzing Maps

3.7.1 Structural Comparison

3.7.2 Map Engineering

3.7.3 Mapping Tools

3.8 Harnessing Collective Intelligence for Knowledge Mapping

4. Data Mining the Semantic Web

4.1 Web Data Mining

4.2 Document Processing

4.3 The Case for Knowledge Representation Structures as Metadata

4.4. The Conceptual Knowledge Base

4.4.1 Introduction

4.4.2 Representational Elements of a Map

4.4.3 Operations with Maps

4.5 Mapping Descriptive Knowledge with Maps

4.5 Concepts in Documents’ classification

4.5.1 Introduction

4.5.2 Concept-Based Information Retrieval

4.5.3 Concept-Based Document Classification

5. Conclusions, Critics and Further Research

6 Appendix

6.1.Acronyms:

6.2 References:

🕸️Web x.0: Web's evolution - Part 1

The Web 2.0 term was proposed by Tim O’Reilly in conference brainstorming session between O'Reilly and MediaLive International, in which he envisioned several competencies. With the new versioning the old fashioned Web, as we know it, became Web 1.0, while in the literature were mentioned other two versions - Web 3.0 for the Semantic Web and Web 4.0 for the Noosphere. There are many people who are not comfortable with the version addressing of the Web, on one side it doesn’t seem natural, while on the other side it’s the easiest manner to encompass the set of characteristics or philosophies in the smallest unit of meaning.

The transition between the different versions is occasionally marked with vague comparisons in which are reflected two or three characteristics, some of them are fixed, while other dependent on authors’ expectations. Into the below table I tried to put together some of the characteristics of what each Web version is about, there are still blank spaces, there are even wild guesses about what the future might bring. The table is not perfect, but it summarizes somehow my understanding about Web’s expectations.

 

Dimension	Web 1.0	Web 2.0	Web 3.0 (Semantic Web)	Web 4.0 (Noosphere)
User participation	read only	read-write	collaborate	human-machine collaboration
Intelligence	Individual Intelligence	Collective Intelligence	Swarm Intelligence	Artificial Intelligence*
Content creation	companies	communities	ecologies	human-machine ecologies
Content focus	owning	sharing	aggregating	reasoning
Indexing	directories/taxonomies	folksonomies	Knowledge Maps	Large Language Models*
User expression	home pages	blogs/webcasts	social networks	?
DIKW focus	data	information	knowledge	wisdom
Macro-focus	document centric	content centric	knowledge centric	wisdom centric
Content accessibility	Web forms	Web Services	meshups	semantic applications
Content presentation	web sites	portals	meshup aggregations	?
Content structuring	HTML	XML	XML programming-based aggregation	XML concept-based aggregation
Vector-based graphics	applets	RIA	RIA 2	semantic RIA
Ads	advertising	pay-per-click	?	?
Information access	searching	subscription (to services)	contextual filtering	prompt engineering*
Knowledge structure	taxonomies	ontologies	networks	Lego-like networks
Data mining emphasis	Web logs	behavior	concept-based	pragmatic

(*) Updared Dec-2025

Probably one major drawback of the above table is that it had no timeline associated with each stage, though it got right the importance of AI in term of collaboration, forming of ecologies, the importance of semantics, reasoning and "wisdom". 

🕸️Web x.0: The Semantic Web (Part I: An Introduction)

Even if I’m just a newbie in Semantic Web and Semantic Technologies, in the past months I had the chance to give some thought to this idea. The way I see it, the Semantic Web targets to make content processable and understandable by machines, and not necessarily targets, at this stage, to evolve the Web to a space of “machine reasoning”, in which machines can replace human reasoning with comparable results. This state of art won’t be achieved also in the next foreseen stage, named Noosphere by a few Web theoreticians, for example [1]. 

The Noosphere, formed from nous (mind) and sphere (space or circle), can be regarded as a “space of human thought” supposed to reflect in real time the dynamics of collective intelligence, the role of visualization (reflection) and aggregation tools being essential. In time, I suppose that machines will grow (in) intelligence, being more and more capable to handle various tasks more like humans. When this will happen?! Who knows… Along the time the world’s theoretical models barriers were pushed beyond previous existing limits, so everything is possible, even braking the barriers of Goedel’s incompleteness theorems.

The Semantic Web is just a stage in the evolution of the Web, same as Web’s versions, it reflects a new way of thinking about Web, its role, expectations and tools supposed to fulfill them. Each person or community can have its own expectations and way of approaching the Semantic Web, lot of effort being spent in different directions, reinventing the wheel, technologies that die soon after they were born. Most of the researchers consider ontologies as the backbone on which the Semantic Web has to be built, many technologies focusing on this perspective. There are also scientists who question the achievability of a Semantic Web or the role of ontologies in this picture. C. Shirky’s [2], supported also by P. Gaendenfors [3], sustains that “the Semantic Web is a machine for creating syllogisms” and therefore it will improve only the areas that uses syllogistic reasoning. 

Conversely, ontologies are just islands of knowledge not anchored in reality, they offer only a view/map of the world, and even if they reflect the commitment to common agreement, they are not a commitment to completeness. Ontologies are supposed to be created mainly by experts, involve high costs, considerable effort and coordination, and it seems that they follow the fallacies of OOP programming, breaking apart in their own complexity and require redesign when new facts are brought into the picture or the scope changes. As new knowledge is acquired or the requirements changes, the work on ontologies never ends, ontologies matching and integration involving other type of issues. Even more, to make things even fuzzier, M.K. Gergman [4] mentions more than 40 information structures that have been labeled in one way or another as ontology – tag cloud, controlled vocabulary, topic map, concept map, etc. Another important aspect neglected by ontologies seems to be the fuzzy nature of truth, while other issues derive from the information representational structure problems: symbol grounding problem, frame problem [5] and contextual emergence [6].

The goal of the Semantic Web is to “get people to use more meta-data” [2], and why not to create metadata, of harnessing the Collective Intelligence, as [7] formulates it. It has started with wikis and folksonomies, and might continue with more complex annotations, for example Knowledge Maps. It is created thus a layer of connectivity on top of physical structure of the Web Graph.

From my point of view Web theoreticians focus on high level goals and ignore the immediate needs of the users, which are often excluded from the Semantic Web equation. Models and technologies that target only the scientific world (e.g. ontologies) have low chances to make a difference in the Web space. The Web users need (free) tools that can be used for metadata creation, collaboration, information processing, knowledge mapping and diffusion. At least in the near future machines won’t achieve the thinking performances of humans about the world, though maybe once the Web riches the state of a Semantic Web, things would be much simpler.

References:
[1] Levy, P. (2005). From Cyberspace to Noosphere. [Online] Available from: http://www.minervaeurope.org/events/parma/papers/levy_ppt.ppt (Accessed: 26 January 2009)
[2] Shirky, C. (2003). The Semantic Web, Syllogism, and Worldview. [Online] Available from: http://www.shirky.com/writings/semantic_syllogism.html (Accessed: 7 February 2009)
[3] Gaerdenfors, P. (2004 B). Conceptual Spaces as a Framework for Knowledge Representation. [Online] Available from: http://www.mindmatter.de/mmpdf/gaerdenfors.pdf (Accessed: 9 January 2008)
[4] M.K. Gergman. 2009. ‘Structs’: Naïve Data Formats and the ABox.[Online] Available from: http://www.mkbergman.com/?p=471(Accessed: 17 April 2008)
[5] Duch, W. (1995) From cognitive models to neurofuzzy systems - the mind space approach. [Online] Available from: http://www.fizyka.umk.pl/publications/kmk/95sams.pdf (Accessed: 13 January 2009)
[6] Atmanspacher, H., Foundation, P., beim Graben, P. (2005). Contextual Emergence of Mental States from Neurodynamics. [Online] Available from: http://www.igpp.de/english/tda/pdf/potsdama12.pdf (Accessed: 13 January 2009)
[7] O’Reilly, T. (2005). What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software. [Online] Available from: http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20 (Accessed: 18 April 2009)