| VKSL is a diagramatic language used to express the essence of any body of knowledge. It uses the
Semantic Network approach of concepts and relations. It exploits the adage that "a picture is worth a thousand
words". The goal of VKSL is to transfer knowledge an order of magnitude faster and more accurately than speech and text. |
Created 7/29/00 - Last Updated 7/30/00 - Jack Harich - Home
Introduction to Knowledge Structures
Knowledge exists in the form of objects and their relations. Languages express knowledge using the familiar "subject verb object" sentence structure. The subject is an object. The verb is the relation between the two objects, which we call concepts. Any body of knowledge can be expressed with a network of relations and concepts, also called nodes and arcs.. Since words are used for relations and concepts, the term Semantic Network fits nicely. For example:
(do - VKSL is a type of Semantic Network is a type of Knowledge Structure)
A Semantic Network is a type of Knowledge Structure. While a Semantic Network must be a network of relations and concepts, a Knowledge Structure can be a network, a grid, a list, a graph, prose, speech, programming code, etc, really any collection of knowledge that has order, which is what structure is. When we use the term Knowledge Structure we allow any of these types, but are referring to the type under consideration, which is usually a network. Note that any type can be converted to a network, and since networks are the fundamental universal type we use them in VKSL.
The type of Semantic Network we use in VKSL is known as a Concept Map.
Some VKSL Examples
VKSL can handle a more complex sentence, where the verb in the first phrase is the object in the second phrase. The sentence is, "The earth revolves around the sun once a year".
(do using fuser or switch)
VKSL shows bodies of knowledge with a dotted enclosure. For example take a look at an early VKSL diagram on VKSL itself. This example also shows how VKSL shows importance, sequence and bodies of knowledge. Of interest is VKSL is a logical third step in the historic progression of language, after speech and text.
The real power of Knowledge Structures lies in their ability to clearly show complex relationships and allow new relations to be gleaned. For example in the following Knowledge Structure the dotted relation is merely implied.
(do)
Now that you've seen this example, we can present A Deep Thought: :-)
| The ability of humans to combine or rework Knowledge Structures and use them to discover new useful relations or substructures is how discovery happens. Speech and text have implied structure, but Knowledge Structures have explicit structure. By making Knowledge Structures explicit instead of implied, VKSL greatly enhances learning and creativity. |
How VKSL Works
VKSL works by reducing the impedence mismatch that causes learning from speech or text to be so hard. The source of knowledge, such as a book or teacher, has its knowledge organized one way. The learner of knowledge usually has their existing knowledge organized another way. Since learning is the integration of new knowledge with old, the large difference between the two pools of knowledge is the impedence mismatch to be overcome every time knowledge transfer (learning) occurs. This difference is greatly reduced by storing the knowledge to be transferred in the form the high level mind thinks in, a Semantic Network.
Another way to say this is speech or text is fairly unorganized knowledge, while the high level mind processes, stores and recalls highly organized knowledge. If new knowledge is not already highly organized, the mind must take the time to do that first.
VKSL is designed to mimic the intermediate Semantic Network the human mind uses as an abstractional layer between external knowledge forms and the deep internal knowledge forms of the Neural Net the brain consists of. While we don't know exactly what the mind's intermediate knowledge layer looks like, we can make an educated guess that it's a very high level summation of the lowest levels, which are the 100 billion neurons connected by trillions of synapses. Since the low level is a Neural Net, the high level summation is probably a Neural Net of some kind, such as the Semantic Network that VKSL is. This is congruent with the discovery that multi level perceptrons (neural nets with stimuli as input and one network per layer) work immensely better than a single level one.
Status
Currently we have about a dozen VKSL documents. Some are a collection about XP and PP (Prescriptive Process). The language symbols are undergoing discovery, with no surprises yet, except for a symbol that can be both a concept and relation. Exactly what VKSL vocabulary works best for knowledge transfer is a large challenge.
We have run one experiment on 20 people that showed VKSL was 26% faster for learning than text. This is part of our strategy to let experimental results drive progress. A second experiment using the PP documents is underway. Our hopeful goal on the second experiment is a minimum of 100% faster.
Currently VKSL documents are very, very slow to create. This will improve as the language stabalizes, becomes richer, VKSL authors gain more expertise, and reasonable tools appear. However even if authorship remains somewhat slow, documents are only created once but learned many times.
| A plesant surprise has been discovering that VKSL forces the knowledge author to focus on essence. This is a huge benefit. It leads to more concise, more understandable, more to-the-point documents, as well as increasing the author's productivity and insights. |
To be continued....
