♾️Cognitive Science: On Crowds (Quotes)

"We are more wicked together than separately. If you are forced to be in a crowd, then most of you should withdraw into your self." (Seneca , cca. 65 AD) 

"The vulgar crowd always is taken by appearances, and the world consists chiefly of the vulgar." (Niccolò Machiavelli, "The Prince", 1513)

"The crowd plays the tyrant, when it is not in fear." (Baruch Spinoza , 1677)

"Public is a ferocious beast: one must chain it up or flee from it." (Voltaire , 1748)

"Every numerous assembly is mob, let the individuals who compose it be what they will." (Lord Chesterfield , 1751) 

"The pretence of collective wisdom is the most palatable of all impostures." (William Godwin, 1793)

"It is easy in the world to live after the world’s opinion; it is easy in solitude to live after our own; but the great man is he who in the midst of the crowd keeps with perfect sweetness the independence of solitude." (Ralph  Emerson, 1840)

"Men, it has been well said, think in herds. It will be seen that they go mad in herds, while they only recover their senses slowly, and one by one." (Charles Mackay, "Extraordinary Popular Delusions and the Madness of Crowds", 1841)

"A mob is a society of bodies voluntarily bereaving themselves of reason." (Ralph W Emerson , 1841) and 

"The nose of a mob is its imagination. By this, at any time, it can be quietly led." (Edgar A Poe, "The Works of Edgar Allan Poe", 1849)

"Those who know that they are profound strive for clarity. Those who would like to seem profound to the crowd strive for obscurity. For the crowd believes that if it cannot see to the bottom of something it must be profound. It is so timid and dislikes going into the water."(Friedrich Nietzsche, "The Gay Science: With a Prelude in Rhymes and an Appendix of Songs", 1882)

"The larger the mob, the greater the apparent anarchy, the more perfect is its sway. It is the supreme law of unreason." (Sir Francis Galton, 1889)

"Good sense travels on the well-worn paths; genius, never. And that is why the crowd, not altogether without reason, is so ready to treat great men as lunatics." (Cesare Lombroso, "The Man of Genius", 1891) 

"If there is a look of human eyes that tells of perpetual loneliness, so there is also the familiar look that is the sign of perpetual crowds." (Alice Meynell, "The Spirit of Place, and Other Essays", 1899)

"Where there had been only jeers or taunts at first, crowds come to listen with serious and sympathetic men." (Edward Carpenter, "My days and dreams: being autobiographical notes", 1916)

"The masses […] neither should nor can direct their personal existence, and still less to rule society in general." (José Ortega y Gasset , 1930) 

"When everything is connected to everything in a distributed network, everything happens at once. When everything happens at once, wide and fast moving problems simply route around any central authority. Therefore overall governance must arise from the most humble interdependent acts done locally in parallel, and not from a central command.A mob can steer itself, and in the territory of rapid, massive, and heterogeneous change, only a mob can steer. To get something from nothing, control must rest at the bottom within simplicity." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Crowds can push the merely good to unearned fame, but they’ll rarely get wholeheartedly behind the terrible." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Beauty is not defined by the masses but by the opinion of the individual." (Rune Leknes)

"Business today consists in persuading crowds." (T.S. Eliot)  

"Crowds are somewhat like the sphinx of ancient fable: It is necessary to arrive at a solution of the problems offered by their psychology or to resign ourselves to being devoured by them." (Gustave Le Bon)

"Educate and inform the whole mass of the people [...] they are the only sure reliance for the preservation of our liberty." (Thomas Jefferson)

"I much prefer the sharpest criticism of a single intelligent man to the thoughtless approval of the masses." (Johann Kepler) 

"In solitude the lonely man is eaten up by himself, among crowds by the many." (Friedrich Nietzsche)

 "It is proof of a base and low mind for one to wish to think with the masses or majority, merely because the majority is the majority. Truth does not change because it is, or is not, believed by a majority of the people." (Giordano Bruno) 

 "If it has to choose who is to be crucified, the crowd will always save Barabbas." (Jean Cocteau)

"In almost every act of our lives whether in the sphere of politics or business in our social conduct or our ethical thinking, we are dominated by the relatively small number of persons who understand the mental processes and social patterns of the masses. It is they who pull the wires that control the public mind." (Edward L. Bernays)

"[It] is impossible for us to establish a living vital connection with the masses unless we will work for them, through them and in their midst, not as their patrons but as their servants." (Gandhi)

 "Men whose counsels you would not take as individuals lead you with ease in a crowd." (Cato)

 "Non-cooperation is an attempt to awaken the masses, to a sense of their dignity and power. This can only be done by enabling them to realize that they need not fear brute force, if they would but know the soul within." (Gandhi)

 "Observe the masses and do the opposite." (James Caan)

"Only those who leisurely approach that which the masses are busy about can be busy about that which the masses take leisurely." (Lao Tzu)

 "Opinions are formed in a process of open discussion and public debate, and where no opportunity for the forming of opinions exists, there may be moods, moods of the masses and moods of individuals, the latter no less fickle and unreliable than the former, but no opinion." (Hannah Arendt)

 "Quotes are empty and meaningless. It is how they are used that gives them purpose, how the person repeating those words gives them meaning. Good quotes do not offer the author immortality. Instead, they give the author limitless rebirths on the tongues of the masses." (Andy Clark) 

 ‎"Two’s a company, three’s a crowd" (saying)

 "The adjustment of reality to the masses and of the masses to reality is a process of unlimited scope, as much for thinking as for perception." (Walter Benjamin)

 "The average man's opinions are much less foolish than they would be if he thought for himself." (Bertrand Russell) 

‎ "The only certainty about following the crowd is that you will all get there together." (Mychal Wynn)

"The man who follows the crowd will usually get no further than the crowd. The man who walks alone is likely to find himself in places no one has ever been." (Alan Ashley-Pitt)

"The mob has many heads but no brains." (Thomas Fuller)

"The wisdom of the masses is not always [...] wise [...]" (Jon Stewart) 

"Truth happens to individuals, not to crowds." (Osho)

"We should not listen to those who like to affirm that the voice of the people is the voice of God, for the tumult of the masses is truly close to madness." (Alcuin, Letter to Charlemagne)

‎"What if the 'wisdom of crowds' turns out to be the ignorance of the masses? In fact, what if the Internet is a 'really bad thing' for the world and its population?" (Stephen Saunders)

 "When a hundred men stand together, each of them loses his mind and gets another one." (Friedrich Nietzsche)

"When distant and unfamiliar and complex things are communicated to great masses of people, the truth suffers a considerable and often a radical distortion. The complex is made over into the simple, the hypothetical into the dogmatic, and the relative into an absolute." (Walter Lippmann)

  

🔖Knowledge Representation: Knowledge Maps (Part VI: Patterns)

A pattern could be considered as a perceptual structure derived from observed similarities existing in the structures existing in a given layer. Following the DIKW pyramid, from data to information, knowledge and further to wisdom and beyond, could be observed various patterns that offer new insights at the respective levels, patterns that could categorize the findings in layers beyond the layers in which they are observed. We could discuss thus about data patterns referring to patterns existing in data, typically aggregated in form of information, information patterns referring to patterns existing in knowledge, typically aggregated in form of knowledge, knowledge patterns referring to patterns existing in knowledge, typically aggregated in form of wisdom and eventually wisdom patterns, referring to patterns existing in wisdom.

The K-map, as a graphical tool used to represent chunks of information/knowledge and the associations existing between them, is not only a container for patterns, but a pattern itself, and this because the various types of K-maps are attempting to address special patterns in knowledge and its representational layout: chained, clustered, hierarchical, radial or networked. Patterns could be observed in the smallest representational elements existing in K-maps, the way the chunks, called nodes, are associated together forming micro-patterns, and further aggregated in creating macro-patterns. Ignoring the content of each node, and the meaning of associations, but keeping for example the direction of the associations, could be observed several simple patterns:

 

The above patterns are not necessarily representative, their scope being to provide an idea of what a pattern looks like: sequence (i), parallel sequences (ii), triangle (iii) & (iv), square (v), divergent (vi), convergent (vii) and any combination of them. For example the simplest pattern, the sequence, could be observed in all the other patterns excepting (xii) and (xiii) patterns, and it could include any numbers of nodes. Maybe more difficult to observe, the patterns (vi) and (vii) could be found in (viii) and (ix), in fact the number of emerging or emerging arrows from node could be greater than two. The number of such patterns could be in theory infinite, the nature offering a huge collection of such patterns, while many of the above patterns could be observed in K-maps too, the arrow playing the role of named or unnamed associations. Here are some variations of the above patterns based on concepts and the associations between them:

The concepts (“Concept i”, i=1, 2, 3, 4) from the above diagram stand as placeholders for any concepts that could fit in such patterns. For example “data”, “information”, “knowledge” and “wisdom” are forming a sequence based on same association which could be named “leads to”, “forms”, “aggregated in”, etc. Causality relations could be modeled with such sequences, for example “high data quality” leads to “accurate report”, which leads to “accurate analysis”, which leads to “better decisions”. However causality is more complex, witness being the various Causal Maps available on the Web, for example “theory”, “concept” and “grounding”, respectively “customer satisfaction”, “product quality”, “customer loyalty” and “brand strength” seems to “break” the sequence pattern. Please note that the associations don’t necessarily to be true, they could represent as well an opinion or be rooted in experiments that confirms them. The laws of causality, complexity and patterns are too complex to be debated in here, in fact each person accumulated in time knowledge that matches such patterns.
  

In the above diagram, could be observed that the examples based on causality are situated at the top, respectively at the bottom of the diagram, in between different types of associations. Is it there any reasons for that? In the moment we asked ourselves such a question, we are already attempting to identify a spatial pattern referring to the positioning in a given area, in this case a diagram. Spatial patterns are at their turn quite complex patterns, we could see it in the world around us. Does the fact that the same distance exists between various points, that forms are contained one in the other or that the associations are dispersed radially from a common point tell us something? What do such patterns represent for us? 

Sometimes we have to make abstraction of the associations and content and see the macro-forms created by clusters of such patterns in order to discern the patterns. Does it seem too complicated? It isn’t at all. Just look at a poem formed of 4 strophes and a common rhyme. We are ignoring in fact what the words represent, the letters of a word, or the points that form a letter, we consider just the form of the strophes and the endings, that’s such a pattern. In nature there are so many such patterns, some even more simple than we might think of, and here are some examples:
 
The world of patterns doesn’t stop here, we could associate patterns related to all our senses, resulting thus olfactory, tactile, auditory or kinesthetic patterns. It seems that such patterns are moving beyond our representational sphere, doesn’t it? Not at all, everything could be reduced to a concept and the associations between them, the difficulty residing in how to choose the “best” pattern that reveals the represented knowledge.

🔖Knowledge Representation: Knowledge Maps (Part V: Propositions)

   Unfortunately the richness of natural language goes above the capabilities of the current representational tools, for example the temporal, spatial, causal and conditional associations, or the associations between whole propositions are not so easy to represent in many of the K-maps available. Given the rules of syntax, in natural language a proposition is formed typically at minimum of a triple, while a typical proposition could be expressed as one or more such triples. On the other side, a chunk of meaning could be represented as one, two or more triples that span within one or more propositions, same as from a whole text only 2-3 triples could be relevant. As chunks could spread along several propositions, it is necessary to identify the meaningful triples and recombine them in more complex constructs, such a construct being the knowlet.

   Because has been discussed in several occasions about chunks of meaning, some examples are necessary in order to understand what is meant by this construct. The simplest chunk of meaning is the concept itself, for example “collective”, “intelligence”, “collective intelligence” and “harnessing collective intelligence” are such chunks. The next logical type of chunk of meaning is a simple triple formed actually from three concepts, for example “harnessing collective intelligence is a web 2.0 principle” is based on “harnessing collective intelligence”, “is a” and “web 2.0 principle”. A minimum of 4 concepts could result in two triples, for example “web as platform and harnessing collective intelligence are web 2.0 principles”, the two triples could be split in different propositions. By adding one more concept we arrive to three triples, and the logic could be followed, resulting several types of patterns.

     Is the latest example still a chunk of meaning? Yes, it is because they share together the same object within the same proposition, enclosed in a definition-like clause. Definitions are typically examples of such unitary chunks of meaning, for example let’s consider the definition of K-mapping given by Dr. Ann Hylton:

    "Knowledge Mapping is the process of surveying, assessing and linking the information, knowledge, competencies and proficiencies held by individuals and groups within an organization." [1]

     Proposition’s structure could be represented in a K-map resembling a Concept Map as follows:

  Knowledge Mapping definition - Concept Map

     The representational language depends from person to person and from one KM to another, for example the same proposition could be rewritten as follows:

Knowledge Mapping definition - Knowledge Map

  Here’s another example following a similar structure, this time based on  D. Hyerle’s definition for K-mapping:

     Knowledge Mapping is “a rich synthesis of thinking processes, mental strategies, techniques and technologies, and knowledge that enables humans to investigate unknowns, show patterns of information, and then use the map to express, build, and assess new knowledge” [2].

 
   Which KM is better is a matter of personal taste, in theory the radial/hierarchical structures are easier to understand and navigate than the networked structures, though the later category of structures is closer to the structure of knowledge, the way concepts are structured. On the other side it could be problematic to represent the two definitions in the same K-map. Here’s an attempt based on propositions’ structure, respectively by restructuring concepts and associations:

 

   Each of the two approaches comes with its advantages and disadvantages, the first K-map being closer to initial definitions’ formulation, highlighting the concepts found closer to the subject, but more difficult to represent, while the second, more condensed, closer to the representation of knowledge as triples, but keeping less from the initial structure. The maps are not necessarily representative, and neither too elaborate, they represent just the raw representation of two definitions. It would be useful to include multiple definitions in the same K-map, attempting to represent all the attributes of a concept and most representative associations. The downside of such K-maps is that they are more complex and all the consequences deriving from it.

References:
[1] M. Jafari, P. Akhavan, A. Bourouni, R. H. Amiri, (2009). A Framework For The Selection Of Knowledge Mapping Techniques. Journal of Knowledge Management Practice, Vol. 10, No. 1, [Online] Available from: http://www.tlainc.com/articl180.htm (Accessed:16 October 2010)
[2] Hyerle, D. (2008). Thinking Maps®: A Visual Language for Learning. In: Thinking Maps®: A Visual Language for Learning, ISBN: 978-1-84800-149-7. [Online] Available from: http://www.springerlink.com/content/x57121720731381j/ (Accessed: 23 June 2009)

🔖Knowledge Representation: Knowledge Maps (Part IV: Associations)

Associations, in several contexts called relations, are links between concepts, and even if they are labeled or not, explicit or implicit, they have assigned a meaning too. The split between concepts and labels introduces  two perspectives:
1. Knowledge broken down to concepts in which some of the concepts function as associations between other concepts.
2. Represented knowledge broken down as labels in which some of the labels function as associations between other labels.

A simple example of association is the one implied by the object agent verb (OAV) construct, also called the object subject verb (OSV), that stands not only at the base of linguistics topology but also at the base of RDF triples (in this context referred as subject-predicate-object, concept-connection-concept or ‘entity-event-entity’) rooted in the linguistics topology. Such constructs are the “is-a” and “has-a” constructs, often used in knowledge representation. For example “whale is a sea mammal” could be expressed as (“whale”, “is-a”, “sea mammal”) and “whale has a tail” as (“whale”, “has-a’, “tail”) in (subject, predicate, object) notation, however representing knowledge as such triples is not an easy task, but the visual representation of such triples with nodes and links reduces the complexity to some degree.

 

The domain knowledge could be relatively expressed as such isolated triples, but in knowledge representation, in order to reduce the complexity of visualization and navigation, it’s simpler to join such triples when they have common concepts. Thus the two triples could be represented as follows:

 
The arrow shows in this case the agent, the first label in the sequence being the subject, while the label in between being the predicate. There are maps that don’t make use of arrows, in some cases the radial flow expressing the direction like in the case of Mind Maps, and maps in which there is no direction implied or a bidirectional row implies a bidirectional association, as in the case of synonymy.

The use of arrows and adequate labeling boxes facilitates KM’s understanding, in what concerns the role of labels in a triple, and navigation, in what concerns the flow/direction. As can be seen from the last diagram a subject could be involved in multiple associations, in the same way an object or predicate could be involved in multiple associations too. In the next KM could be observed for example how the same predicated is involved in multiple associations describing the anatomy of a whale.

 

Whale Knowledge Map (adapted after whale anatomy)

The original representation could function as a K-map as well, though images are more difficult to process than the maps built with adequate software, the later offering also the possibility of conversion to a portable format that could be further processed. In addition the spatial disposition of concepts could play a role as well, in this case being correlated with the disposal within whale’s anatomy. In more complex KMs the use of “background” images is not always easy to embed, in addition the multitude of connections increasing the overall complexity. The form of representation could depend on each person’s preferences, the association could be explicit as well as implicit, and typically only one of them is use. Here are the same “has-a” associations represented with the help of circle map (implicit associations) and bubble map (explicit associations).

Bubble map	Circle Map

The “is-a” and “has-a” associations are used in combination with any other types of associations, of importance being especially the causality (A causes B), synonymy (A is synonym of B) or antonymy (A is antonym of B), precedence (A precedes B) or concomitance (A occurs at the same time as B), etc. In fact any verb, substantive or even prepositions could play in theory the role of an association. If the above examples fall in the “verb as association” category, the use of substantives as associations is maybe more difficult to intuit, so here is an example based on whale’s anatomy in which the “has-a” has been replaced with “anatomic part” association:

 
Between two concepts there could be in theory multiple associations, more than one explicit association, though few are such cases because typically is stressed only the most important/relevant association. In the below image, a part of a KM on “self-transcending knowledge”, could be seen how the “competitive advantage” is involved in two associations with the same concept.

Self-transcending knowledge KM part (KM based on [1] text)

The existence of multiple associations has several other implications in what concerns associations’ type. For example causality implies two inverse associations: “A causes B”, respectively “B caused by A”, in fact dealing with the same meaning associated in different directions by inversion of terms. Such constructs could be confusing, therefore a good practice is to adopt only one of the two associations; the simplest approach is to simply use “A causes B”. A similar type of association is the transposed association, in which from “A imply B” is inferred that “Not-B imply Not-A”. With this we entered in the territory of deductive reasoning, entailment and of rules of inference. 

Deductive reasoning could prove to be quite complex and of great use, especially when is intended to infer new associations (inferred associations) based on an existing set of associations. For example if in a KM we have that “A implies B” and “B implies A”, then we could deal with the equivalent association “A equivalent to B”, in mathematical terms expressed as “A=B”. Another simple logical inference is based on the simple rule of inference: if “A imply B” and “B imply C”, then “B imply C”. Implication could be applied also to causality, synonymy and several other types of associations.

The fact is that many of the rules of inference that apply is deductive reason could be used to KMs too, special inference engines could be used for this purpose. Associations between two or more concepts don’t have to be of the same type in order to prove to be useful, or in some cases even if the association seems to be of different types, the meaning they carry could be sufficient to allow an association to participate in inferences, this being valid especially for the associations belonging to the same class of meaning. In addition, associations of the same type and the concepts involved could reveal interesting properties that could be analyzed from the perspective of (superior) algebra or network theory.

Cardinality of Associations

The above representations have one important issue - they don’t reflect the cardinality of concepts, how many elements of the same concept participate in the associations. For example the whale has two blowholes and two pectorial fins, while a table has for legs, etc.  In database modeling the associations, actually called relations, include the cardinality (e.g. 1-to-1, 1-to-n, n-to-n) though it just highlights that there is one or multiple records/entities associated in relations. In our case is typically required to specify the actual cardinality. As database model could be regarded as KM too, it’s thus necessary to address both types of cardinality, when they apply.

References:
[1] A. Kaiser, B. Fordinal. (2010). Creating a ba for generating self-transcending knowledge. Journal of Knowledge Management.Vol.14, no. 6 [Online] 10.1108/13673271011084943

🔖Knowledge Representation: Knowledge Maps (Part III: Concepts and Labels)

Knowledge could be broken down to concepts and the associations between them, words functioning as links between concepts. A concept could be seen as a unit or chunk of meaning, for example “mother”, “father”, “child”, “home” are a few of the concepts we incorporate in our conceptual vocabulary, the collection of concepts we hold in our mental world. The previous four mentioned examples are just words, though words are just labels, transporters of meaning, and they don’t equate to concepts. A word (written or spoken) same as an image, a symbol or sound are just a representation/externalization of a concept outside of the mental world, we could regarded them simple as labels. Sure, they are strong correlated, and concepts could exist without labels, same as labels could exist without the articulation of a concept in the mental world. In a simpler formulation we could say that:

Label + Meaning = Concept

Meaning and labels are concepts too, fact that makes the definition kind of redundant, but that’s less important as long the overall meaning is understood. In addition the concept of meaning is quite complex, meaning being considered as existing within a given context, a context that carries its own meaning and it’s a concept in itself. More redundancy, isn’t it?

We could in theory put on a paper all the concepts we know, to be more focused, all the concepts we know related to other concept functioning as topic. Such constructs are similar to the ones of tag clouds, user-generated tags used to describe the content of a web page. Another type of such collection of concepts is the shopping list or any other types of lists.

 

Example of tag cloud	Example of shopping list

                               

Each of the words from the above tag cloud, respectively shopping list, are labels of the concepts they represent. In contrast to the shopping list, the size of the labels used in the tag cloud is proportional with their occurrence, but that’s less important for now. The fact is that both are representing a collection of concepts. More complex collections could be based on concepts derived from scientific works, book indexes are another such of example.

In the above shop list, “movie tickets” is a multipart label for a “single” concept, though it’s composed of the labels of two different concepts – “movie” and “ticket”. In this way concepts/labels could be formed by the aggregation of meaning for concepts, respectively concatenation of labels for labels. In theory we could put together as many of concepts or labels as we wish, though there are some limits imposed by linguistics. In daily life aggregations/concatenations of 2-3 concepts/labels are pretty usual: “knowledge mapping” (= “knowledge” + “mapping”), “knowledge management”, “business intelligence”, “collective intelligence”, “harnessing collective intelligence”, etc. There are also examples of labels encompassing already a concatenation of other labels, words formed with prefixes (e.g.: “un” + ”believable” = ”unbelievable”), affixes (“engineer” + ”ing” = ”engineering”) or hybrid/compound words (e.g. “auto” + ”mobile” = “automobile”)  are the simplest examples we used on a daily basis, some of the languages being more abundant than the others in such compounds (e.g. German language is quite rich in such hybrid words). 

The linguistics and semantic meaning of words offer us a deeper overview on the formation of words and meaning, though we don’t have to go that far. I will just limit myself to mention that a morpheme is the smallest compound of a word, composed at its turn of multiple phonemes, the smallest linguistically distinctive units of sound, and graphemes, the smallest units of written language. In addition words or labels does not necessarily have to respect the rules of language morphology as a whole, one of such compounds that entered in my vocabulary many years ago, comes from the Disney’s musical film Mary Poppins, and those who the movie, probably already intuit what I’m talking about: Supercalifragilisticexpialidocious is formed, according to Wikipedia, from the following morphemes: super- "above", cali- "beauty", fragilistic- "delicate", expiali- "to atone", and docious- "educable", and the sum of these parts signifying roughly "atoning for educability through delicate beauty".

Given the richness of languages in what concerns the morphology, syntax and declension, how do we choose the labels? Also this is a complex topic and my knowledge stops somewhere. I prefer to use the infinitive form for verbs, nominative and singular form for substantives, sometimes called the dictionary forms, and include the prepositions when they change the meaning (English language is full of such constructs).  It’s not always so easy to do that, but I try to stick to it whenever is possible. In what concerns the labels formed of multiple other labels I prefer to choose the smallest unit of meaning that defines a concept “uniquely”.

Classes of Meaning

 A class of meaning equivalence, shortly class of meaning, comprises all the labels that share the same meaning [1]. Such a class comprises all the synonyms, acronyms and forms of construction of natural language used to label a concept. For example, depending on context, any of the following labels could be considered as belonging to the same class of meaning: abode, apartment, flat, home, mansion or residence. Many of us use acronyms in daily life, with formal or informal character: EOD (End of Day), ASAP (As Soon As Possible), US (United States, Uncle Sam, Upstream, Ultrasound), KM (Knowledge Map, Knowledge Management, Knowledge Mapping, kilometre), etc. 

The syntagm “forms of construction of natural language” refers primarily to the various elements of orthography  hyphenation, capitalization, word breaks, emphasis and punctuation, which introduce some variance in the labelling of concepts. On whether we write “knowledge based” or “knowledge-based”, “center” or “centre”, “Knowledge Management” or “knowledge management”, “class of meaning” or “meaning class”, the various labels refer to the same concept. Normally in a Map would be enough to include only one member of a class of meaning, unless is intended to highlight the synonymy or any other similar association type.

🔖Knowledge Representation: Knowledge Maps (Part II - Knowledge Mapping)

It’s natural to talk about K-maps within Knowledge Representation, and extensively within Knowledge Management, domains in which the process of creating a K-map is known as Knowledge Mapping, shortly K-mapping. In essence that’s what K-mapping is about, creating a K-map, however in literature could be found more elaborated definitions. According to D. Hyerle, K-mapping is “a rich synthesis of thinking processes, mental strategies, techniques and technologies, and knowledge that enables humans to investigate unknowns, show patterns of information, and then use the map to express, build, and assess new knowledge” [1]. 

Within the same context but from a slightly different perspective, [3] regards it as a “consciously designed communication medium using graphical presentation of text, stories, models, numbers or abstract symbols between map makers and map users”. In the context of K-mapping in organizations, B. Bergeron defines it as the “process of identifying who knows what, how the information is stored in the organization, where it’s stored, and how the stores of information are interrelated” [2]. All the above definitions focus on mapping as a process, knowledge as the object of the mapping process and the context in which it occurs.

The use of various types of K-maps introduces similar concepts, K-mapping being referred as concept mapping when talking about concept maps or when the granularity of a map is at concept level, mind mapping when the focus is Mind Maps or externalization of mental maps, semantic mapping when discussing about semantic maps, process mapping in the case of process maps, etc. Related concepts stress other aspects of K-mapping referring to different levels of abstraction, for example ontology creation or ontology engineering, in this context ontology mapping referring to the mapping between different ontologies. Same happens in the world of the databases in which data modeling or semantic modeling refers at the creation of a data model, while data mapping refers at the mapping between two structures.

As stressed before, the various types of K-maps in use introduce their own creational philosophy, which with a little effort could be raised at the state of methodology. K-mapping as a process could be reduced to input, output and what happens in between. As input for a K-map could be used the various types of media content, mental models, knowledge of experts, discussions outcomes or any other sources of information. As knowledge is not always available at our disposal, searching for knowledge, identifying knowledge is quite a time consuming task, especially when knowledge is not indexed, easily accessible or we don’t exactly know what we are searching for. In general we talk about knowledge acquisition, which refers to the process of extracting knowledge, structuring and organization of knowledge. 

When creating a K-map we are doing extensively knowledge acquisition, essential in the process of learning. In exchange, when we create K-maps on the mental models, we are externalizing our knowledge, transforming the tacit in explicit knowledge, figuring out or better said evaluating what we stored in our brain, grounding of knowledge by finding meanings, formalizing concepts, finding the best formulation, finding new associations, building on previous knowledge, examine beliefs, integrating, mixing and recombining knowledge, identify patterns in knowledge, patterns of thinking, becoming creative, etc. Also these aspects are part of K-mapping, even if they are not so visible in the process, however they are stressed especially during K-mapping. 

The structuring and organization of knowledge in K- mapping is done in representational form (visualization), translated into spatial organization or simple aggregation of information, in patterns or models of expressions, and here the various types of K-maps are used as a form of expression. Mapping techniques need to be flexible in order to reflect the representational richness of knowledge, they evolve with experience, “the one who maps” learning with time to take advantage of the appropriate type of K-map or pattern. K-mapping is thus an iterative process, a K-map being evolved during several stages, the final outcome being unexpectedly different than the inputs or expected outcomes.

K-mapping applies to individuals as well as to groups, collaborative or coordinative K-maps involving more complex forms of acquisition, process or visualization, often being involved some forms of negotiation and knowledge sharing from which the output emerges. Collaborative K-mapping seems to be preponderant to the contexts in which the force of the group emerges, mainly economical and scholar organizations, and recently social networks.

References:
[1] M. Jafari, P. Akhavan, A. Bourouni, R. H. Amiri, (2009). A Framework For The Selection Of Knowledge Mapping Techniques. Journal of Knowledge Management Practice, Vol. 10, No. 1, [Online] Available from: http://www.tlainc.com/articl180.htm (Accessed:16 October 2010)
[2] B. Bergeron. (2003). Essentials of Knowledge Management. John Wiley & Sons, Inc. ISBN: 0-471-28113-1.
[3] H. P. Tseng, Y-C. Lin (2008) A Knowledge Management Portal System for Construction Projects Using Knowledge Map. Knowledge Management:
Concepts, Methodologies, Tools and Applications, M.E. Jennex (Ed.). ISBN-13: 978-1-59904-934-2.

🔖Knowledge Representation: Knowledge Maps (Part I - An Introduction)

Most of us are familiar with the geographic Maps, during school hours or outdoor trips we had at least once the possibility of guiding ourselves using a Map. Paraphrasing Alfred Korzybski, “the map is not the territory”, though a Map offers a bird’s eye view of the territory, it might be not perfect, nor so detailed as we would like it to be, though it allows people identifying their position, their destination, their route left behind and the one ahead, the obstacles, the boundaries, the known and unknown zones. Our marvelous memory allows us storing details of such Maps, more or less perfect copies of the Maps, voluntarily or involuntarily memorized. 

Even more, we are creating in our brain an adaptable dynamic Mental Map of the world we live in, reflecting the changes occurred in it the way we perceive them. It is what Robinson, quoted by [1] calls it “reduction of reality” and “construction of an analogical space”, the complex structure of reality being reduced to an easy to memorize Map attempting to be a reflection of reality. A geographic Map uses Names, color, spatial organization, and a small set of symbols in order to represent a projection of the geographical world we live in. In a similar manner we could map also the various types of knowledge using a similar set of tools known as Knowledge Maps or simply K-maps.

Especially between students, could be met the practice of underlining or marking a chunk of text with special marker(s), highlighting the respective text as important, at least for a second review. The chunk of text could be a concept, a definition or a whole paragraph, and that’s actually the input for a K-map. In theory you could put the whole chunks of texts together on post-it or electronic documents, group them together in some way, and there you have a rudimentary K-map. Thus could be intuited that a K-map is a graphic organizer of chunks of texts identified as information or knowledge. Putting together row text allows structuring the content based on identified associations, it could be sequential structure, logical implication, importance, topic, etc. 

Thus are created implied or explicit association between the various chunks of text. Further value could be added by breaking the text in smaller chunks, summarize a chunk in fewer words, replace words with more meaningful words, add numbering, symbols, other types of markings (e.g. NB = “nota bene”, QED = quod erat demonstrandum), references, questions, etc. Of course, the same could be done in the book itself though ignoring the fact that book’s owner or further readers could argue such methods, the value comes from having the chunks of text coming from different books in one place or, if you want, in fewer text containers, and the electronic documents make this approach such an easy task.

From a visual perspective, processing a whole chunk of text could be time consuming, especially when we look for specific information, typically key words, primary concepts, definitions, etc. Marking such text in special ways brings some additional value, though the linear character of text makes it still difficult to process. What if we break the text at conceptual level? Wouldn’t such output be easier to process? Now it depends also on each person’s capabilities, though working with concepts seems closer to the mental structure of our mind, in the way meaning is represented and created. 

What if we further represent the associations between concepts explicitly, giving them names, and thus associating other concepts with them? Wouldn’t be such structures easier to process and understand? Such structures would be also K-maps, though their content is more refined. Actually the concept-based and whole propositions could be mixed together in K-maps, the degree of refinement of such concepts being more a figure of taste or, as will be further seen, of philosophy.

Until now we considered a K-map as being an aggregation of chunks of text of various granularities, implied or explicit associations, and the later could be labeled using other more or less standardized concepts. We talked also about formatting, meaningful display, resources, symbols and other types of markings. These are in fact the content elements of a K-map, but in definitive what is a K-map?!

Extrapolating the above considerations, a Knowledge Map or K-map could be considered as a visual graphical tool used to aggregate and represent information or knowledge. The definition seems to need further refinement because the graphical character implies a visual component, representation could involve aggregation and thus the later term could be abandoned, while following the DIKW pyramid, knowledge involve information. Graphical is the form of representation, while the role of visual is to represent explicitly the channel of communication, in fact stressing its visual, respectively graphical character, a K-map could be considered as synonym to visual aid or graphical organizer, terms more frequent used, especially in teaching. No matter of the degree of knowledge encompassed, a representation of the knowledge is not the knowledge itself, it resumes thus to information that triggers knowledge and the associations existing between information. Thus, a K-map could be defined as a visual graphical tool used to represent information and the associations existing between them, either implicit or explicit. The term information encompasses here any type of symbols or chunk of texts. When information is present in its most granular form, at concept level, the K-map is a visual graphical tool used to represent concepts and the associations existing between them.

The definition of a K-map represent the “what” from the W5H1 syntagm, how about the why, who, when, how, by whom and by what means? Why a K-map, isn’t the text or what we know enough? Do we really have to break the knowledge into such maps? Maybe we don’t, it depends on each persons capacities, some of us have a really good memory, retain everything they read and recalled it any time. For others, such capabilities come with some effort, spending some time in memorizing the information we consider as useful for the future, and also this step depends on each person’s capabilities and skills. As mentioned in introduction, the read material could be “formatted”, broken into pieces, annotated, summarized, restructured in order to increase the efficiency of memorization and recall. 

According to researchers, in brain itself takes place some unconscious restructuring of information, associations are created, strengthened and removed, the later activity resulting in forgetting the information once stored, the recovery of such information necessitating a review of the source or sources used in the first place to acquire the respective information. Considering the huge amount of information our brain deals with, it’s almost impossible to identify during a simple read, all the connections existing between the various concepts assimilated, especially when they aren’t so evident. This perspective of what’s happening in our brain is quite simplistic, though can be discovered already some gaps in the learning process, gaps which in theory could be addressed with the help of K-maps. As its name and definition denote, a K-map has the function of a map, used to represent knowledge. 

Once such a map created, it would be easier for us to access it, and thus to refresh our information, eventually use it as a reference to the actual text. In addition, when evaluating the associations of such a map, existing or inexistent, we could identify new associations, conditions under which they hold (e.g. range of applicability, exceptions), new concepts, new contexts, etc. Let’s not forget that the human is a social being, the meaning of the concepts we deal with relying also their meaning at macro level – organizations, communities of practice, friends or any other types of groups. The individual maps could be used in order to compare and evaluate knowledge, collective collaborative and coordinative creation of K-maps coming with their benefits too. They could be used as a baseline for learning, negotiation, documentation or (self-) evaluation. 

The creation of a K-map requires additional time, time we don’t always have. Does it really make sense to create such a map all the times? It’s probably recommended to create it when we acquire new knowledge, especially when we want to identify the concepts on which the respective chunk of knowledge is built upon. Once the backbone concepts mastered, the necessity of a map decreases to some degree, in the end its necessity depending on individual needs. A K-map could be useful also when externalizing the knowledge, especially the tacit knowledge, in organizations being quite a valuable tool in documenting the various types of information organization work with, process maps, flow maps, value stream maps, being several examples of such K-maps.

How to create a K-map? In definitive maybe we create such maps without knowing it, it’s built in our “ADN” as we often arrive to express complex thoughts by externalize them in diagrammatic form. There are more than 50 types of K-maps available in the literature: Concept Maps, Semantic Nets, Conceptual Graphs, Mind Maps, and so on. They have many similarities, often the similarities residing in the philosophies used to create them. Adhering to one or more of such maps is a question of need, preferences, habitude and requirement. Sometimes also deviations from the philosophy behind a map could prove to be useful as long satisfies a purpose, same as a map arrives to be misused, for example by placing too much content, be it irrelevant or relevant, or of using too much formatting, not respecting guidelines, etc. There are several requirements a K-map should satisfy – it should be simple to use and navigate, with an approachable level of complexity and thus understandability, adaptive and dynamic.

 “By what means” could refer here to the tools used to create K-maps and the channels used to distribute them. Again it’s a question of need, preferences, habitude and requirements. In the past years appear many tools used for the creation of K-maps, having multiple features, but still lacking in representational content the human mind is used to. The paper and a crayon could prove efficient as well, while those with an exceptional memory could create such maps directly in the inner mental world. Everything is possible, everything it’s a question of practice and self-improvement of techniques.

References:
[1] Hyerle, D. 2008. Thinking Maps®: A Visual Language for Learning. In: Thinking Maps®: A Visual Language for Learning, ISBN: 978-1-84800-149-7. [Online] Available from: http://www.springerlink.com/content/x57121720731381j/ (Accessed: 23 June 2009)

🌐 Meta-Blogging: Is Blogging Stagnating?

Introduction

When I started this blog, it wasn’t in my intent to do meta-blogging (blogging about blogging), though a few days ago, while browsing through Linkedin posts, Nic Oliver’s question “Where have all the bloggers and commenters gone?”  draw my attention. The question is rooted in his observation that a few social sites, he was member of, are registering an apparent decrease in the number of blogs and comments. Is it really this happening? The question preoccupies me not only as blogger and owner of a blog on web-related theme, but also as I’m interesting in the evolution of web and its trends. 

Until now I had no reasons to pose such a question, I mean even if the current blog had few visitors in the past month, this was also a consequence of the fact that lately I haven’t managed to post something new, however my sql-troubles blog acknowledges a considerable increase in the number of visitors, from about 50 users per week at the beginning of this year to about 250 users currently. I know that’s not a big number when compared with other professional blogs, though for beginning that’s even a little more than I expected. I could corroborate the increase in the number of visitors with the increase in the number of posts and the fact that I tried to post each week something.

A Look at Personal Navigation History

Looking back at my navigation history of the past months I have to recognize that I focused more on professional blogs, mainly on the MSDN blogs which are going through a considerable boom, probably a result of a change in strategy coming from Microsoft. Spending more time on content creation and reading of a several profession-related books, the time spent on reading others’ blogs decreased considerably. I even kind of neglected Linkedin in the detriment of Facebook, though the time spent on Facebook decreased considerably lately because of the lack of time corroborated with saturation in what concerns the wanted and not wanted content, from the later category I remark the Farm Ville and Mafia games content. I could add also the relatively small number of new features added to Facebook, feeling that something more could be done in this direction, especially in what concerns content filtering, categorizing and aggregation. I am remarking these facts also because the respective problems could apply to other social networks or blogging too, and from this point of view I have several person observations.

Personal Blogging Concerns

I’m using Blogspot since almost 5 years now, it’s a nice blogging platform, though with a few exceptions the number of important new features since then is quite small, lacking several important features. The best example is the editor which provides few built-in html functionality when compared with the HTML tags available in HTML4 or newly HTML5. Sure, the HTML editor supports in theory all HTML tags though they need to be entered manually, and this equates with considerable effort from my part in some cases, especially when I consider the programming language code that needs special formatting in order to be easily readable. I found myself in the position to appeal from necessity to an editor like Windows Live Writer, though it has its issues too. To content formatting adds content presentation, in this category falling blog’s layout, quite inflexible from some points of view, and content categorization. Labeling and clouds are great in order to highlight important keywords, though I feel something more could be done in this direction, for example aggregating labels in categories or in Knowledge Maps.

Another important problem for myself is commenting, and here are several aspects. First, and maybe the most important aspect related to users authentication for commentaries, some of the blogs request a user to be logged in in order to comment, sure some of the websites are integrated with Facebook, Twitter or myOpenID credentials, though not all the users have and want to create a new account for this purpose (SideWiki it’s out of discussion here as it’s not integrated directly in the blogs). Secondly, the comments have poor or inexistent rich formatting, often resuming to text messages. Third aspect: it would be useful to have more flexibility in what concerns the notification when a new comment is posted.  Fourth aspect: also comments require sometimes some categorization, especially in what concerns in filtering out adulatory or irrelevant content. Fifth aspect: annotation, one of the important features of the Read-Write web is almost inexistent.

Saturation Point

All the above aspects could in theory make users a little unsatisfied with the blogging experience and reflect thus in a possible decrease in the number of blogs, posts or comments. In order to understand what’s happening we have to go deeper and understand the value of blogs for both, bloggers and readers, the important link between the two being the content. I was thinking that maybe we reached a content saturation point in which bloggers have less to say, the important subjects being exhausted. Maybe we reached also a break-even point between request and demand in what concerns the content reached an equilibrium in some domains, the variety and coverage of content facilitating this. Maybe the readers lost their interest, in definitive they want to be entertained, otherwise they’ll find another entertainer. Now not sure how many bloggers want to be in the position of an entertainer…

Time and Satisfaction as Drivers

As I was highlighting above, also the available time for blogging is a problem as good content and comments requires time, often some research in addition to the personal experience, from the later perspective the subjects could drain, bloggers having nothing more important to say (I actually have seen a few blogs closed because of that). The inverse ratio between time spent on one side and financial or personal satisfaction on the other side is possible to have depleted over time. For sure many people are blogging because they have something important to tell the world on professional and non-professional themes. It’s not only the interest but also some satisfaction involved, either financial or appreciative. 

The financial outcomes are quite small, resulting from ads and number of clicks gathered, the relevance of ads’ content being quite an important factor, and here I’m having some personal complaints too. The appreciative satisfaction is somewhat reflected in the number of visitors, the comments they post and the value they have for the blogger, and I would say that’s quite an important factor. I have also to remark the destructive/pejorative intent of some of the comments, many readers lacking in some basic blogging and social web ethics, being inclined to denigrate rather than criticize constructively. To this adds the volume of spam, the use of captchas, excepting their role of reducing the quota of spam, have the side effect of annoying the commenter, in some situation the use of captchas being totally badly-designed.

Content Absorption

A considerable number of blogs were just reflecting personal non-technical events from bloggers’ life, social networks like Facebook absorbing increasingly such content, and from this perspective I have to recognize that they are more appropriate for this purpose. Probably many commenters moved to the (social) professional networks, having more freedom in posting the questions, and potentially more experts, and thus a higher number and diversity of opinions, plus the feedback seems to function better. In addition, the professional nature of such networks could bring in theory more value than the blogs, the marriage between blogs and professional networks being the logical and maybe necessary move, though this requires a richer integration between the two platforms.

Behavioral Change

Maybe readers have started to do some research by themselves or learned to use online encyclopedias like Wikipedia that offer a good inverse ratio between content on one side and complexity, variety, spread, navigation on the other side. Maybe the users moved to the video content, richer in visual and auditive experience, many universities, companies, professionals and non-professional posting their webcasts or radiocasts online, collections like YouTube/Edu being a good example. Or maybe the users are too demoralized because of the crisis, sure that’s a forced possible cause, though in the end also the socio-economical factors should be considered.

Some Statistics

I was wondering how many bloggers moved their blogs between the various platforms or they totally abandoned their blogs or how many readers comment actually what they read. Even if I’m a little reticent to the way some statistics on the web reflect the reality as long I don’t know the background and the way they were collected, they could be used at least as approximation/trending. Technorati through its State of Blogosphere annual report seems the most quoted source for such statistics, though the report for 2010 it’s not already available, while the statistics for the previous years seems to be dispersed across several posts. Here are some important facts related indirectly to the current topic quoted by SEJ from Technorati:

- 77% of Internet users read blogs
- 70% of all respondents say that personal satisfaction is a way they measure the success of their blog
- the most common rate of updating is 2-3 times per week

Unfortunately no number in what concerns my three above concerns. Anyway, several blogs, for example Caslon Analytics blogging and PCMag.com, are quoting an old report coming from Perseus, in which is estimated that about 66% of the blogs are temporarily or permanently abandoned after two months. I wanted to dig more into the topic but the time and the ocean of information, in which is still difficult to search for relevant information, made me stop here…

Learning: Random Thoughts on Knowledge and Learning

Kristen Weatherby, sharing her thoughts after Reform event held in UK, writes on “The Teachers Blog” in “Knowledge versus Learning” post:

“I believe very strongly that education is about the transfer of knowledge from one generation to the next.”

“Knowledge is the basic building block for a successful life…. What is to be criticised is an education system which has relegated the importance of knowledge in favour of ill-defined learning skills.”

 I’m not a teacher, even if I coquetted with the idea of teaching, and it’s not (yet) in my attributions to criticize or defend the past or future of education, however as a simple mortal who spent some time in schools, I hope I’m allowed a few observations.

Taking the first quote from above I would like to start with two other quotes that worth to meditate on:

“Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?” (T.S. Eliot,  "The Rock")

 “Information is not knowledge. Knowledge is not wisdom. Wisdom is not truth. Truth is not beauty. Beauty is not love. Love is not music. Music is the best.” (Frank Zappa)

Knowledge is not the most important asset that we have to transmit to further generations, but the wisdom we’ve acquired, teaching them how not to make the same mistakes that we did. Philosophers and spiritual leaders attempted in many books to reveal the importance and the many facets of wisdom, highlighting in parallel the limitations of knowledge. Spending our lives on the quest of knowledge has quite a high price that might cost us the future of our children. Also wisdom might not be enough, but together with love, beauty and music, could make a difference.

Coming back to the actual subject of this post, I strongly believe that it's important to make the educational system adaptable to the changes occurring in our society, to create an infrastructure that sustains and embraces change. In many countries the educational systems remained the same for years, even decades, or the changes are insignificant when compared to the changes occurred in our society, especially in the area of technologies, business’ dynamics or culture. Change is inevitable, and there is a time for change, propitious for change, and when this time passes, the system is forced to change from outside, and then there are also victims. It’s not a personal theory, but the laws of nature - an educational system is part of another ecosystem, which at his turn is part of a bigger system, and so on, thus a change occurring in an upper system has greater impact in lower ecosystems if the respective ecosystems are not prepared to support the change. The actual economical crisis is maybe the best example in this direction.

     Something needs to change, but what? We are living in a world overloaded of information, we are attempting to derive knowledge from information, but the sea of information is so deep that we risk to sink, we are attempting to process information using the processing power of computers and that’s not so easy, as the attempt of mimicking meaning, which has an important role in the identification of knowledge, it’s still in its baby steps. Einstein was saying something like "a problem cannot be solved at the same level (of consciousness) it was created" ( I hope is the right quote).  Until we find the right solution to the big problem, we could try to solve the common small issues we deal with.

    The debate between memorization of facts and figures, respectively personalized learning, in fact could be reduced to the attempt to find a balance between quantity and quality – quantity as a measure of what is learned, and quality as a measure of what remained after. (Maybe it’s not the best formulation, but the meaning is close to what I intended to say.)  In theory all we have to do is to optimize the equation based on the two, and most probably many other aspects (e.g. financial, personal or technological resources). Normally the solution is somewhere in between, and rarely at extremes. In this case, again, at least in theory, should resume in using effectively and efficiently what we have, in strengthening the weaknesses.

    For me as a student and later as hobbyist “researcher”, the most time consuming activities are searching for information, indexing and mapping the information found. I’ve been spending lot of time for searching simple definitions, definitions that should help us define the world we live in. In addition, often it’s not easy to fish for the facts, having to read a whole book in order to find 2-3 important ideas. For sure we need to simplify the content, to personalize it to the degree of interest and understanding for each person, but in order to reveal the networked structure of network, I think that we need to provide the same type of structural knowledge navigability. Wikipedia is somehow providing that, but it’s just a start.  Only relatively I was introduced in the world of visual representation of knowledge. 

Various types of simple tabular diagrams, circle, trees or brace maps have been used for decades in almost all fields, but I have hardly seen in manuals or teachers to refer to more complex knowledge maps like Mind Maps, Cognitive Map, semantic networks or Vee-diagrams, and I feel that they better reflect the structural patterns of knowledge than the text. The promoters of such maps insist on the importance of associations, visual rhythm, patterns, colour or spatial awareness, on the role of left brain vs. right brain, on the cognitive processes, the methods of memorizing, how knowledge is created, stored, processed, mixed and remixed, on how to collaborate in order to solve problems. Making people aware of these is a step, and it starts with learning people how to learn, on how to use the tools and resources effectively and efficiently, on how and where to find information, how to take notes and map knowledge. I don’t think this requires a huge amount of investment, and there are schools that made important steps in this direction.

The personalization of the learning process is required by the different learning needs, of the different aptitudes and attitudes toward learning. For many people learning could be regarded as an unpleasant endeavor, I think that some effort must be spent in order to make learning pleasant, to make things "attractive to learn" – games work well for small and mature alltogether. In addition many students don't see the necessity of learning so many things, there should be a trade between theory, utility and applicability. For example in Mathematics behind each theory there is a problem that the respective theory attempts to solve, graduating in Math, I found the applicability of many of the theories learned only many years later. 

Talking about University, I've seen that the professors were often avoiding, most probably from lack of time, to explain people the grounds of theories, but expecting people to understand the grounds even if they are not so simple to see. From time to time I was hearing a professor asking himself how is it possible that the students don't understand the grounds behind the theories, how they can't perceive such basic stuff. Of course, the true is somewhere in between, what I'm trying to point here is that not for everybody all the facts are evident. Especially in Mathematics we are talking about several hundred/thousands years of evolution in mathematical thought, in teaching of Mathematics being avoided subjects like: why, when, where, how, by what means, questions essential in acquiring of knowledge.  There were also professors who preferred that students learn less material, that they understood the principles behind each theory, that they can make use of the knowledge they have. I think is important to invest effort in making knowledge, implicitly also knowledge resources, available at the level required by a student.

 Looking into the future, at the impact technologies have in our life, I expect that the role of a teacher changes from knowledge provider to enabler/facilitator of the learning process, of course this most probably can't be done starting from the first grade but introduced gradually. This doesn't mean that there will be no more need for skilled teachers, only the roles will change, and I feel that will be a win-win outcome.

🔖Knowledge Representation: DIKW (Part I: An Introduction)

Collocations like “Web of data”, “Web of Information” “Web of Knowledge” “Data Management”, “Information Management”, “Knowledge Management”, “Data Technologies”, “Information Technologies”, “Knowledge Technologies” and other similar such compounds involving Data, Information and Knowledge (DIK) are increasingly and interchangeably used in various contexts by vendors, customers, researchers and bloggers, sometimes being misused from confusion or exaggeration. On the other side it is true that the border between data, information and knowledge can be shallow, and from here rise up divergent opinions and theories, and as always people like to fight about their ideas and believes. To the three adds wisdom forming what is known as DIKW, the Knowledge Pyramid, however wisdom is less met in the technical landscape presumptively because it’s harder to express and quantify.

DIKW reveals the layered evolution of information from data to knowledge and further to wisdom; it’s just a wide accepted model, a pyramidal representation to which there were attempts of including proto-facts and facts [2], understanding [1] [6], transaction and intuition [7], vision and “One Mindness” [6], and probably other similar attempts might be there in the deep web. Sometimes such theories are pure philosophy apparently without applicability and quite far away from the work of scientists who are used to juggle with data, information and knowledge, relatively easy to quantify and grasp compared to wisdom, intuition, meaning, creativity, vision, consciousness, awareness or any other similar terms that made, since ancient times, philosophers’ delight. 

On the other side the scientific work is built upon the philosophic work, and seemingly as new advancements are made the “New Age” philosophies tend to be integrated in scientific work. From the concepts from the second group, only meaning has started to stir up researcher’s attention, and this mainly in AI domain, which attempts to bridge the gap between humans and machines. I suppose there are attempts also in the area of creativity, vision, intuition or wisdom, though it will be difficult to do concludent steps until the barriers of meaning are not broken.

Data is considered to be “simple isolated facts” [4], a combination of "symbols or isolated and non-interpreted facts" [5], symbols, raw that “simply exists and has no significance beyond its existence” [10], quantified or qualified symbol sets, signals or sensory stimuli perceived [3], etc. Be it facts, symbols, events, signals or sensory stimuli, it is highlighted their isolated and not-interpretable characteristics. When we think of data we refer mainly to the written symbols, independent on whether they have or not meaning for us – a digit or set of digits, a letter and the words they form, an image or any combination of them. In a apparently collection of signs, each sign could be regarded as a data chunk, though it happens that we decipher patterns of signs that we know, for example words, phone numbers, addresses, coordinates, time, etc. In database terminology data refers to a matrix structure (columns vs. records) in which each cells of the matrix contains a data chunk that nowadays could be any aggregation of signs and data types a database system supports. 

 As we can see interpretations of data could have different granularity dependent on the form of aggregation and purpose, thus a natural question: when could we discuss about information? For sure a collection of data is not information, then what ingredient(s) is/are essential in having a chunk of information? I believe that there are two magic words – relation and meaning, actually meaningful relation. Using various algorithms, and even from a simple review of the data, researchers could establish various relations between two data chunks, for example that they always occur together, that one causes the other, that XYZ is the address of John Smith, and so on. 

To make it even more complicated, for an IT professional, a structure, and I’m referring here mainly to databases, supposes already the existence of relations between data, thus statements like XYZ is the address of John Smith who was born on 1st of July and works for ABC are already available. Of course, only few relations are useful, just because two chunks of data occur together, in one or several locations, this doesn’t mean that such a relation is meaningful, while in other situations that might be quite important. 

When we discuss about a meaningful relation we actually imply a context, a multidimensional (mental) frame in which meaning is formed and derived from. Thus a relation could be meaningful in one context and irrelevant in another, and that’s why many say that data don’t equate with information. Discovering that the evolution from data to information is context dependent could maybe solve some of the philosophical disagreements, though this aspect makes it difficult to find a common accepted definition for information. Basically information is just chunks of data found in (meaningful) relation(s).

 In what concerns knowledge things are not less complex, the just formulated definition for information could be adapted from my point of view also to knowledge, advancing the idea that knowledge is information found in meaningful relation. Establishing meaningful relations between information implies a certain level of understanding, conscious or unconscious processing and systematization of information, their integration in the existing mental structures - contexts. Mental structures apply here not only to individuals but also to communities, in the past years the later gaining considerable importance. 

Communities equate with networks, and networked is also the structure of the knowledge, this involving a dynamic (evolutional) nature, with multiple values of truth, levels of organization and granularity. The systematization and integration of knowledge relies on existing or new formed patterns based on characteristics of knowledge. There were written many books about knowledge, trying to depict its characteristics, and there are so many facets that it’s difficult to find a definition that accommodates all the types of knowledge – tacit vs. explicit knowledge, personal vs. networked knowledge, declarative vs. procedural, practical vs. theoretical knowledge, personal vs. collective knowledge, contextual, situational or definitional knowledge, and so on. I find it quite important to understand the various types of characteristics because each of them comes with their own characteristics and uses.

Wisdom is skillful use of knowledge, knowledge put into use, transcended knowledge, understanding the patterns and principles of knowledge, knowing when, where, how, why and by what means to apply the knowledge. Wisdom involves also keeping the balance between the various aspects of situations, strengths and weaknesses altogether, minimizing or totally overcoming biases. In spiritual literature and tradition, wisdom plays a fundamental role, the many books on this topic attempting to reveal the qualities (dimensions) and implications of wisdom. I will limit myself only to give two quotes, namely one from A. Einstein who was remarking that "wisdom is not a product of schooling of the lifelong attempt to acquire it", and B. Russel was remarking that “although our age far surpasses all previous ages in knowledge, there has been no correlative increase in wisdom” [8].

The new added scales to the DIKW pyramid are questionable, it’s kind of a gray area, based maybe on deeper considerations not so easy to grasp, while the lack of definitions and argumentation makes the understanding process even more difficult.  The best example regarding the lack of definitions is the proto-fact, in such situations we could eventually call into help the possible etymological formation. The prefix proto-, from Greek protos, when used in compounds has the meaning of “first” or “before”, thus that proto-facts could be considered as basic/primary facts, maybe axioms, idea that matches also [5]’s view, who’s considering them as assumptions that are used to extract new facts from the world. Following this definition or assumption, proto-facts and facts are rather information, data describing them at most so I really don’t see why the facts and proto-facts are considered as the lower layer on which data layer is based, this because I see data as describing facts, mainly as quantitative or qualitative associations.

From a creationist point of view, everything is vibration, what we perceive is nothing but grossier vibrations taking various forms, some of them recepted and processed by our senses. The layer that comes into contact with the medium (environment) is called by [7] the transaction layer, where the “packets” of sensations are put onto or taken off of the medium, and before any data can exist, the packets have to be received, unpacked and assembled into data. Even if the denomination of transaction layer is not maybe the best, and the description is quite simplistic, this should be at high level the process in which the stimuli from the outside and inside world are transformed by our senses and brain in the so called “data”, forming our continuous reality. [7] believes that somewhere at this level the intuition occurs, though it could be also at higher level as long everything happens within the unconscious mind. Without going to deep into philosophical debate I would limit myself to say that I find well-founded the adding of intuition into DIKW scale, and I could put it on the same level with creativity, innovation, and vision, all three dimensions transcend knowledge and have their important role in understanding and further in wisdom’s attainment. The role of intuition and creative is reinforced also within Edward de Bono’s Six Thinking Hats, red hat representing the intuition, while the green hat, the creativity.

 Vision and “one mindness” seem to come from the New Age vocabulary though they are not so far away from modern science, vision in technical terms is reflected in forecasting, approximating the future based on past trends, while “one mindness” is the principle or the state of art behind Noosphere. It’s true, in what concerns the human being and the actual stage of development, such terms sound to be inapplicable, on the other side when we consider that everything is in continuous evolution/involution, everything is possible. From the point of view of machines (computers), given their interconnectedness and processing power everything is possible.

When we look from a relational point of view, data are symbols with no explicit relation between them, information are data with an explicit relation, knowledge is relational patterns of information, wisdom is patterns of knowledge, intuition is the comprehension of patterns without sensing the patterns consciously, creativity is the discovery or transmutation of new patterns, and maybe it worth to mention also innovation, seen by [9] as the rearranging of relations. More difficult to define from the perspective of relations are the “one mindness” and symbols. The “one mindness” could be eventually regarded as a reflection of the interconnectedness and comprehensions of all existing patterns, including the divine patterns in case of humans. Symbols are at their turn a pattern but of a lower refinement, at form-energetic level. The complexity or relations or patterns denotes the overall complexity, though our senses already hide a higher level of complexity, and most probably here are the barriers of our spiritual and scientific worlds.

In what concerns the meaning, symbols can have or not meaning, or different meanings in different contexts, same could be said about data, information and eventually knowledge, but I would expect that at wisdom and upper levels meaning is fundamental, even conscious or unconscious. With the evolution from data to wisdom and further, the meaning and the relations become richer and more complex. And we can’t discuss about meaning without discussing about context. I tend to believe data have an extrinsic context given by the medium in which they occur, though that’s not necessarily relevant. As per my above given definition, information supposes the existence of a meaningful relation and thus the placement in one or more contexts, while with the integration within a context we could talk already about knowledge.  Probably wisdom acts like a bridge point between contexts, visions extrapolates contexts while in “one mindness” everything becomes one context.

Most of the resources I’ve seen focus mainly on the evolution from data to wisdom, though there is also a feedback mechanism, for example the more high quality data we collect the more are the chances to validate current theories or discover more generalized theories, enriching thus our luggage of information, possibly further evolved to upper layers. It’s interesting also to study the interaction between the various layers, for that having to take into account also the existing theories of learning, of dynamic systems, etc. 

Note:
    I left out understanding from this post because is a complex (philosophical) concept even if everybody has an idea what is about. Please note that I haven’t tried to review the existing DIKW models, but just to write down my thoughts, to create a basis for further posts.

References:
[1] Wikipedia. (2009). DIKW. [Online] Available from: http://en.wikipedia.org/wiki/DIKW (Accessed: 7 May 2009)
[2]  Visual Concept. (1998). Making Meaning. [Online] Available from: http://www.visual-concept.co.uk/makingmeaning.htm (Accessed: 10 February 2009)
[3] Zins, C. (2007). Journal of American Society for Information Science and Technology, 58(4): 279-293, 2007. Wiley InterScience. [Online] Available from: http://www.success.co.il/is/zins_definitions_dik.pdf (Accessed: 16 May 2009)
[4] (Ken) McLennan, K. J.. "Chapter 20 - Knowledge". The Virtual World of Work: How to Gain Competitive
[5] M. J. Coombs ,  H. D. Pfeiffer ,  R. T. Hartley (). e-MGR: an architecture for symbolic plasticity. Int. J. Man-Machine Studies http://www.cs.nmsu.edu/~hdp/PDF/plastic.pdf
[6] OneMind. (2009). Seven Levels of Interaction [Online] Available from: http://onemind.com/2009/12/19/ (Accessed: 7 June 2010)
[7] G. Czerepak. (2007) TIDIKW. [Online] Available from: http://relationary.wordpress.com/2007/07/14/dikw (Accessed: 8 June 2010)
[8] B. Russel. Knowledge and Wisdom
[9] [Online] Available from:  http://thingamy.typepad.com/sigs_blog/2010/01/information-knowledge-wisdom-and-innovation.html (Accessed: 14 June 2010)
[10] G. Bellinger, D. Castro, A. Mills (2004) Data, Information, Knowledge, and Wisdom. [Online] Available from: http://www.systems-thinking.org/dikw/dikw.htm (Accessed: 14 June 2010)