This collection of books is a small, but growing example of the millions of books, journals, periodicals, and academic papers we are currently tracking and indexing. We are updating this library constantly. Please return often and review this library as it evolves.
This library is the first step on our way to making Mathesis Universalis (the long-sought search for a unifying principle in all that we know and experience) a reality. Soon our knowledge representations, in their many forms, will be accessible from the pop-up dialogues which appear when you click/tap on a book’s entry in the overview you see. Other types of knowledge sources; such as social media activity, websites, E-Mails, audio, video,… are also going to be included in our tracking and indexing system.
I, Carey G. Butler, have been working on this idea since 1989 during my study of Foundational Mathematics and Complex Analysis. I then moved to Germany in 1990 to continue my study (this time in the original German language) of many German mathematicians and philosophers such as Carl Friedrich Gauss, Bernhard Riemann, Gottfried Wilhelm Leibniz,… During that first year I began to formalise the idea as I began to learn the German language more intensively.
The journey has been a long one and was finally conceptually refined in March of 2009. The journey has taken many, many turns in the years since. I have made several key discoveries in mathematics, philosophy, and linguistics along the way which I, due to my concerns about priority, have not yet published. A few of these discoveries are documented elsewhere though, but I have been very careful to withhold many aspects of their details until I was able to find and/or adapt current technologies to bring them to a useful expression and application in their fullness. I was confronted with many obstacles and challenges, but I never gave up. For more information about my plans, please visit our website at Mathesis Universalis in English or Mathesis Universalis auf Deutsch.
As it stands, we could have created this library application a year ago. It is, in itself, nothing very special for programmers who know how to build applications like it. However, as we developed and tested the predecessor to this application, it soon became very clear that the sheer volume of data being manipulated was creating increasing demands on the conventional technology we were using at the time. Our concerns about processing time, network speed, and infrastructural demands forced us to step back and create a better foundation which could be scaled to any degree. We have spent the last 8 months (August 2021 – April 2022) building a ‘symphony’ of cloud applications and an infrastructure which is now fast, reliable, and scalable.
Towards that aim, this library represents the ‘orchestration’ of a collection of cloud apps we created or have forked and modified for our purposes that is distributed over several servers. Its backend is primarily driven by a distributed Couchbase database to store and to manipulate the massive amount of data. Also other kinds of databases are implemented for temporary storage or for the frontend’s presentation and housekeeping.
We are currently developing a Progressive Web App which will use this library as one of its sources to present our knowledge representation.
Finally, after 13 years of investment of all kinds and, on the 8th birthday of one of my most important discoveries (‘We Have a Heartbeat‘),…
This system is quite interesting if we allow ourselves to talk about the qualities of infinite sets as if we can know their character completely. The problem is, any discussion of an infinite set includes their definition which MAY NOT be the same as any characterisation which they may actually have.
Also, and more importantly, interiority as well as exteriority are accessible without the use of this system. These ‘Hyperreals’ are an ontological approach to epistemology via characteristics/properties we cannot really know. There can be no both true and verifiable validity claim in this system.
Words are symbolic indications and/or conveyors of meaning and are not that meaning in themselves.
Meaning is found, stored, and manipulated in our minds. This is why different languages are capable, in varying degrees of usefulness, to convey meaning which is very similar to that found via the symbols of any other.
It It is also the reason why there are words indicating meaning that are not found in other languages; or, if found in a different language, the other language requires more of its own structure, dynamics, and resonance to convey the same meaning.
For example: the words ‘déjà vu’ in French are found in German ‘schon gesehen’ and in English ‘already seen’, but these phrases do not convey the full meaning found in the French version. To counter this deficit, their meaning in other languages must be ‘constructed’ out of or ‘fortified’ by the careful use of longer strings of symbols. This additional construction and/or fortification may even fail at times. This is often where the word phrase from a different language is simply added to the language in which the concept is missing.
This same situation is found in the literature of many languages. The words used to convey meaning are condensed and may contain more meaning than is usually the case. In this regard, even the person reading/hearing the words may not possess the competence necessary to catch this condensed meaning in its fullness.
Mathematical expressions, albeit more precise, are also indications of meaning. They are more robust in their formulation, but at ever-increasing depth or scope, even they may fail to reliably or conveniently convey meaning.
Our understanding of what words mean is not always accurate, but where our mutual understanding of the meaning of words overlaps, and the degree to which they overlap, is where their meaning can be shared.
Our own personal understanding of words is measured by our ability to apply their meaning in our lives.
There is also a false meme, which I would like to clarify.
“Knowledge is Power!”
It is wrongly said that ‘Knowledge is power’. The truth is another: Knowledge is the measure of usefulness of what we understand and is the only true expression of its ‘power’.
The value of Knowledge is found in its usefulness and not in its possession.
Yes, in knowledge representation, the answer is the interior of a holon.
Ontologies go ‘out of scope’ when entering interiority. The common ontological representation via mathematical expression is 1/0.
When we ‘leave’ the exterior ontology of current mathematics by replacing number with relation, we enter the realm of interiority.
In the interior of relation, we access the epistemological aspects of any relation.
As an aide to understanding – Ontology answers questions like: ‘What?’, ‘Who?’, ‘Where?’, and ‘When?’. Epistemology answers questions like: ‘Why?’ and ‘How do we know?’
In vortex mathematics 1/0 is known as ‘entering the vortex’.
There are other connections to some new developments in mathematics involving what is called ‘inversive geometry’.
Example: (oversimplified for clarity)
If we think of say… the point [x, y, z] in space, we may assign x, y, and z any number value except where one of these coordinates gets involved in division where 0 is not allowed (up to this point in common mathematics) as a denominator. x/z is not allowed when z=0, for example.
Now, if we are dealing with interiority, numbers are replaced by relationships, such as [father, loves, son].
What if the son has died? Is the relationship still valid?
The answer to this question lies within the interior of those involved in the relation.
They are wonderful tools to explain much of our world, but lack ‘The Right Stuff’ to handle the metaphysical underpinnings of anything near a Philosophy of Mind, Philosophy of Language , or a Philosophy of Learning.
This is, because Category Theory specialises on roughly half of the Noosphere. It does a wonderful job on exteriority, but cannot sufficiently describe nor comprehensively access interiority.
Therefore, as is the case with Semiotics, has limited metaphysical value with respect to philosophy in general.
For example:philosophies of mind, language, or learning are not possible using only category theoretical tools and/or semiotics.
4.4.2 Knowledge is the Terminal Object of Visualisation states:
“The ultimate purpose of the visualisation process is to gain Knowledge of the original System. When this succeeds (when the diagram commutes) then the result is a ‘truth’ relationship between the Knowledge and the System. When this process breaks down and we fail to deduce correct conclusions then the diagram does not commute.”
I want to also comment on Figure 3 (which also exposes missing or false premises in the paper), but I will wait until I have discussed the assertions in the quote above which the authors of this paper reference, accept, and wish to justify/confirm.
1) The purpose of a representation is NOT to gain knowledge; rather, to express knowledge. Also, truth has nothing to do with knowledge except when that value is imposed upon it for some purpose. Truth value is a value that knowledge may or not ‘attend’ (participate in).
1a) The ‘truth value’ of the System (‘system’ is a false paradigm [later, perhaps] and a term that I also vehemently disagree with) does not always enter into the ‘dialogue’ between any knowledge that is represented and the observer interpreting that knowledge.
2) The interpretation of a representation is not to “deduce correct conclusions”; rather, to understand the meaning (semantics and epistemology) of what is represented. ‘Correct’ understanding is not exclusive to understanding nor is it necessary or sufficient for understanding a representation, because that understanding finds expression in the observer.
2a) ‘Correct’, as used in this paragraph, is coming from the outside (via the choice of which data [see Fig. 3] is represented to the observer) and may have no correspondence (hence may never ever commute) whatever to what that term means for the observer.
The authors are only talking about ontologies. That is a contrived and provincial look at the subject they are supposing to examine.
There may (and usually are) artefacts inherent in any collection and collation of data. The observer is forced to make ‘right’ (‘correct’) conclusions from that data which those who collected it have ‘seeded’ (tainted) with their own volition.
‘System’ (systematising) anything is Reductionism. This disqualifies the procedure at its outset.
They are proving essentially that manipulation leads to a ‘correct’ (their chosen version) representation of a ‘truth’ value.
I could tie my shoelaces into some kind of knot and think it were a ‘correct’ way to do so if the arrows indicate this. This is why paying too much attention to a navigation system can have one finding themselves at the bottom of a river!
The paper contains assumptions that are overlooked and terms that are never adequately defined! How can you name variables without defining their meaning? They then serve no purpose and must be removed from domain of discourse.
Categorical structures are highly portable, but they can describe/express only part of what is there. There are structure, dynamics, and resonance that ontology and functionalism completely turns a blind eye to.
The qualities of Truth, Goodness, Beauty, Clarity,… (even Falsehood, Badness, Ugliness, Obscurity,…) can be defined and identified within a knowledge representation if the representation is not restricted to ontology alone.
In order to express these qualities in semiotics and category theory, they must first be ontologised funtionally (reduced). Trying to grasp them with tools restricted to semiotics and category theory is like grasping into thin air.
That is actually the point I’m trying to make. Category Theory, and even Semiotics, each have their utility, but they are no match for the challenge of a complete representation of knowledge.
Here an excerpt from a short summary of a paper I am writing that provides some context to answer this question:
What Knowledge is not:
Knowledge is not very well understood so I’ll briefly point out some of the reasons why we’ve been unable to precisely define what knowledge is thus far. Humanity has made numerous attempts at defining knowledge. Plato taughtthat justified truth and belief are required for something to be considered knowledge.
Throughout the history of the theory of knowledge (epistemology), others have done their best to add to Plato’s work or create new or more comprehensive definitions in their attempts to ‘contain’ the meaning of meaning (knowledge). All of these efforts have failed for one reason or another.
Using truth value and ‘justification’ as a basis for knowledge or introducing broader definitions or finer classifications can only fail.
I will now provide a small set of examples of why this is so.
Truth value is only a value that knowledge may attend.
Knowledge can be true or false, justified or unjustified, because
knowledge is the meaning of meaning
What about false or fictitious knowledge? [Here’s the reason why I say no.]
Their perfectly valid structure and dynamics are ignored by classifying them as something else than what they are. Differences in culture or language even make no difference, because the objects being referred to have meaning that transcends language barriers.
Another problem is that knowledge is often thought to be primarily semantics or even ontology based. Both of these cannot be true for many reasons. In the first case (semantics):
There already exists knowledge structure and dynamics for objects we cannot or will not yet know.
The same is true for objects to which meaning has not yet been assigned, such as ideas, connections and perspectives that we’re not yet aware of or have forgotten. Their meaning is never clear until we’ve become aware of or remember them.
In the second case (ontology): collations that are fed ontological framing are necessarily bound to memory, initial conditions of some kind and/or association in terms of space, time, order, context, relation,… We build whole catalogues, dictionaries and theories about them: Triads, diads, quints, ontology charts, neural networks, semiotics and even the current research in linguistics are examples.
Even if an ontology or set of them attempts to represent intrinsic meaning, it can only do so in a descriptive ‘extrinsic’ way. An ontology, no matter how sophisticated, is incapable of generating the purpose of even its own inception, not to mention the purpose of the objects to which it corresponds.
The knowledge is not coming from the data itself, it is always coming from the observer of the data, even if that observer is an algorithm.
Therefore ontology-based semantic analysis can only produce the artefacts of knowledge, such as search results, association to other objects, ‘knowledge graphs’ like Cayley,…
Real knowledge precedes, transcends and includes our conceptions, cognitive processes, perception, communication, reasoning and is more than simply related to our capacity of acknowledgement.
In fact knowledge cannot even be completely systematised; it can only be interacted with using ever increasing precision.
[For those interested, my summary is found at: A Precise Definition of Knowledge – Knowledge Representation as a Means to Define the Meaning of Meaning Precisely: http://bit.ly/2pA8Y8Y
Universal Constants, Variations, and Identities (Dimension)
#18 Dimension is a spectrum or domain of awareness: they essentially build an additional point of view or perspective.
We live in a universe of potentially infinite dimension. Also, there are more spatial dimensions than three and more temporal dimensions than time (the only one science seems to recognize). Yes, I’m aware of what temporal means; Temporal is a derived attribute of a much more fundamental concept: Change. One important caveat: please bear in mind that my little essay here is not a complete one. The complete version will come when I publish my work.
The idea of dimension is not at all well understood. The fact is, science doesn’t really know what dimension is; rather, only how they may be used! Science and technology ‘consume’ their utility without understanding their richness. Otherwise they would have clarified them for us by now.
Those who may have clarified what they are get ignored and/or ridiculed, because understanding them requires a larger mental ‘vocabulary’ than Physicalism, Reductionism, and Ontology can provide.
Our present science and technology is so entrenched in dogma, collectivism, and special interest, that they no longer function as they once did. The globalist parasites running our science and technology try their best to keep us ‘on the farm’ by restricting dimension, like everything else, to the purely physical. It’s all they can imagine.
That’s why many of us feel an irritation without being able to place our finger on it when we get introduced to dimension. We seem to ‘know’ that something just doesn’t ‘rhyme’ with their version.
Time and space may be assigned dimensionality, in a purely physical sense if necessary, but there are always underlying entities much deeper in meaning involved that are overlooked and/or remain unknown which provide those properties with their meaning. This is why the more sensitive among us sense something is wrong or that something’s missing.
Let us temporarily divorce ourselves from the standard ‘spatial’ and ‘temporal’ kinds of ‚dimension’ for a time and observe dimension in its essence.
Definitions are made from them: in fact, dimensions function for definitions just as organs do for the body. In turn, dimension has its own set of ‘organs’ as well! I will talk about those ‘organs’ below.
Dimension may appear different to us depending upon our own state of mind, level of development, kind of reasoning we choose, orientation we prefer, expectations we may have,… but down deep…
Everything, even attributes of all kinds, involve dimension. We must also not forget partial dimension such as fractals over complex domains and other metaphysical entities like mind and awareness which may or may not occupy dimension. Qualia (water is ‘wet’, angry feels like ‘this’, the burden is ‘heavy’) are also dimensional.
Dimensions are ‘compasses’ for navigating conceptual landscapes. We already think in multiple dimension without even being aware of it! Here’s is an example of how that is: [BTW: This is simply an example to show how dimension can be ‘stacked’ or accrued. The items below were chosen arbitrarily and could be replaced by any other aspects.]
♦ Imagine a point in space (we are already at 3d [x,y,z]) – actually at this level there are even more dimensions involved, but I will keep this simple for now.
♦ it moves in space and occupies a specific place in time (now 4d) 3d + 1 time dimension
♦ say it changes colour at any particular time or place (5d)
♦ let it now grow and shrink in diameter (6d)
♦ if it accelerates or slows its movement (7d)
♦ if it is rotating (8d)
♦ if it is broadcasting a frequency (9d)
♦ what if it is aware of other objects or not (10d)
♦ say it is actively seeking contact (connection) with other objects around it (11d)
♦ … (the list may go on and on)
As you can see above, dimensions function like aspects to any object of thought.
Dimensionality becomes much clearer when we free ourselves from the yoke of all that Physicalism, Reductionism, and Ontology.
Let’s now look at some of their ‘organs’ as mentioned above as well as other properties they have in common:
They precede all entities except awareness.
Awareness congeals into them.
They form a first distinction.
They have extent.
They are integrally distributed.
They have an axial component.
They may appear as scalar fields.
Their references form fibrations.
They are ‘aware’ of self/other.
Their structural/dynamic/harmonic signature is unique.
They provide reference which awareness uses to create perspective meaning.
You need only be ‘connected’ or have something to say or do that our controllers value.
In this one we have 3 points:
1) ‘Huh’ and its variants appear in 31 languages
2) People stop for clarification in conversation once every 90 seconds
3) People share the burden of fixing misunderstanding in conversation.
Now if you didn’t know this already, then celebrate!
‘Trust’ For Sale
More of Google’s attempt to become the ‘clearing house’ of truthful, ‘trustful’, and important facts and therewith create a ‘truthful tribe’. I thought we wanted to rid ourselves of tribalism?
So many talented people will never be known, because they work ‘under the radar’ or for being ignored (exiled) as ‘heretics’.
Here is a question: how can even truth, not to mention trust, be systematized when we cannot know all of it as well as its many sources of origination?
Google is creating its own demise with this. It will go down or cause a vast migration of awakened (and non-evangelists) to move to, create, or participate in other search engines.
True research must make it’s own decisions upon what is truthful, trustworthy, and valuable. If we allow a corporation to manage these values, we will enter an age of ‘privatized credibility’.
They will be able to keep people out of the debate (social discourse) by making them non-authoritative. If they can establish metrics then everyone must conform to them.
It’s like believing Marx, Engels, and Lenin were philosophers when they were really children playing with snake-oil in order to sell the idea that a tyranny of Communism was the solution to humanity’s problems.
Ontology: Compelling and ‘Rich’
They are only surfaces, but they seem to provide you with depth.
This exquisite video shows how the representation of knowledge is ripe for a revolution. I’ve written about this in depth in other places so I won’t bore you with the details here unless you ask me in the comments below.
Stay tuned! I’m behind in my schedule (work load), but I’m getting very close just the same. I will publish here and elsewhere.
I’m going to use this video (and others like it) to explain why ontologies are not sufficient to represent knowledge.
Soon everyone will acknowledge this fact and claim they’ve been saying it all along! (In spite of the many thousands of papers and books obsessively claiming the opposite!!!) They do not know that how dangerous that claim is going to be. Our future will be equipped with the ability to determine if such claims are true or not. That’s some of the reason I do what I do.
Physics is only complex, because it’s in someone’s interest to have it that way.The way to understanding, even if you don’t understand science, was paved with words. Even if those words led only to a symbolic form of understanding.
I’m a mathematician and can tell you that common ordinary language is quite capable of explaining physics. Mathematics is simply more precise than common language. It pays the price for that precision by being subservient to the causal and compositional relations. These are limitations that metaphysics and philosophy do not have.
Words in language have a structure that mathematics alone will never see as it looks for their structure and dynamics in the wrong places and in the wrong ways. Pure mathematics lacks an underlying expression of inherent purpose in its ‘tool set’.
With natural language we are even able to cross the ‘event horizon’ into interiority (where unity makes its journey through the non-dual into the causal realm). It is a place where mathematics may also ‘visit’ and investigate, but only with some metaphysical foundation to navigate with. The ‘landscape’ is very different there… where even time and space ‘behave’ (manifest) differently. Yet common language can take us there! Why? It’s made of the ‘right stuff’!
The monological gaze with its incipient ontological foundation, as found in pure mathematics, is too myopic. That’s why languages such as category theory, although subtle and general in nature, even lose their way. They can tell us how we got there, but none can tell us why we wanted to get there in the first place!
It’s easy to expose modern corporate science’s (mainstream) limitations with this limited tool set – you need simply ask questions like: “What in my methodology inherently expresses why am I looking in here?” (what purpose) or “What assumptions am I making that I’m not even aware of?” or “Why does it choose to do that? and you’re already there where ontology falls flat on its face.
Even questions like these are met with disdain, intolerance and ridicule (the shadow knows it can’t see and wills to banish what it cannot)! And that’s where science begins to resemble religion (psyence).
Those are also some of the reasons why philosophers and philosophy have almost disappeared from the mainstream. I’ll give you a few philosophical hints to pique your interest.
Why do they call it Chaos Theory and not Cosmos Theory? Why coincidence and not synchronicity? Why entropy and not centropy?
… Why particle and not field?
(many more examples…)
I appreciate the technology (as long as it isn’t weaponized) and even admire what has been accomplished thus far. Just I know that to get to the ‘promised land’, they’re going to need to transcend and include the ontologically-based methodologies as are shown in the video!
One trip to Google translate reveals this to be mere hype at present. Hidden Markov models aren’t going to do it, people! That’s like trying to do a radar scan of the ocean and only seeing things you’ve been told to see beforehand. Their example involves capital cities and the meta-framing necessary to differentiate them. Essentially they are building structures (like fingerprints) of ideas and trying to do an ‘algebra’ with them.
The AI paradigm must be ‘fortified’ by epistemologically-based perspectives and methodologies, before we can even think of cognition. Clearly they are already involved in the recognition process, but these missing elements in in artificial intelligence is originating from those doing the work in the video (through their intentions, desires, success criterion,…) without their even noticing it! (Or if they do, they don’t make that clear to the viewer.)
Also, they believe in the mysticism that we need only create the necessary initial conditions (like a soup) and then, through emergence (which they cannot define precisely), intelligence (like life) will pop out!
They will most certainly manage to get the technology to a point that it will become useful (after they’ve shelled out huge sums of money to get there), but they will never reach cognition this way. They will have to part with one of their most sacred dogmas first: the mind is the brain.
The brain is only a part of what we call mind. Our whole bodies are involved with the dialog of mind – from our brains right down to our digestive tracts and even cells (and their constituents).
What is this video about?
In this introductory video I would like to explain what knowledge representation is, how to build and apply them. There are basically three phases involved in the process of building a knowledge representation. Acquisition of data (which includes staging), collation and the representation itself. The collation and the representation phases of the process are mentioned here, but I will explain them further in future videos.
You are now watching a simulation of the acquisition phase as it collects and stores preliminary structure from the data it encounters in terms of the vocabulary contained within that data. Acquisition is a necessary prerequisite for the collation phase following it, because the information it creates from the data are used by the collation algorithms which then transform that information into knowledge.
The statistics you are seeing tabulated are only a small subset of those collected in a typical acquisition phase. Each of these counters are being updated in correspondence to the recognition coming from underlying parsers running in the background. Depending upon the computer resources involved in the
acquisition, these parsers may even even run concurrently as is shown in this simulation.
The objects you see moving around in the video are of two different kinds: knowledge fields or knowledge molecules. Those nearest to you are the field representations of the actual data being collected called knowledge fields. They could represent an individual symbol, punctuation, morpheme, lexeme, word, emotion, perspective, or some other unit of information in the data. Each of them contain their own signature – even if their value, state or other intrinsic properties are unknown or indeterminate during the acquisition.
Those farther away from the view are clusters of fields which have already coalesced into groups according to shared dynamically adaptive factors such as similarity, relation, ordinality, cardinality,…
These ‘molecules’ also contain their own set of signatures and may be composed of a mixture of fields, meta-fields and hyper-fields that are unique to all others.The collation phase has the job of assigning these molecules to their preliminary holarchical domains which are then made visible in the resulting knowledge representation. Uniqueness is preserved even if they contain common elements with others in the domain they occupy. Clusters of knowledge molecules and/or fields grouped together are known as ‘knowledge domains’, ‘structural domains’,’dynamical domains’ or ‘resonance domains’, depending upon which of their aspects is being emphasized.
We now need a short introduction to what knowledge representation is in order to explain why you’re seeing these objects here.
What is Knowledge Representation?
Knowledge representation provides all of the ways and means necessary to reliably and consistently conceptualize our world. It helps us navigate landscapes of meaning without losing our way; however, navigational bearing isn’t the only advantage. Knowledge representation aids our recognition of what changes when we change our world or something about ourselves. It does so, because even our own perspective is included in the representation. It can even reveal to us when elements are missing or hidden from our view!
It’s important to remember that knowledge representation is not an end, rather a means or process that makes explicit to us everything we already do with what we come to be aware of. A knowledge representation must be capable of representing knowledge such that it, like a book or other artifact, brings awareness of that knowledge to us. When we do it right, it actually perpetuates our understanding by providing a means for us to recognize, interpret (understand) and utilize the how and what we know as it relates to itself and to us. In fact – knowledge representation even makes it possible to define knowledge precisely!
What Knowledge is not!
Knowledge is not very well understood so I’ll briefly point out some of the reasons why we’ve been unable to precisely define what knowledge is thus far. Humanity has made numerous attempts at defining knowledge. Plato taught that justified truth and belief are required for something to be considered knowledge. Throughout the history of the theory of knowledge (epistemology), others have done their best to add to Plato’s work or create new or more comprehensive definitions in their attempts to ‘contain’ the meaning of meaning (knowledge). All of these efforts have failed for one reason or another. Using truth value and justification as a basis for knowledge or introducing broader definitions or finer classifications can only fail. I will now provide a small set of examples of why this is so.
Truth value is only a value that knowledge may attend.Knowledge can be true or false, justified or unjustified, because knowledge is the meaning of meaning. What about false or fictitious knowledge? Their perfectly valid structure and dynamics are ignored by classifying them as something else than what they are. Differences in culture or language make even make no difference, because the objects being referred to have meaning that transcends language barriers.
Another problem is that knowledge is often thought to be primarily semantics or even ontology based! Both of these cannot be true for many reasons. In the first case (semantics): There already exists knowledge structure and dynamics for objects we cannot or will not yet know. The same is true for objects to which meaning has not yet been assigned,such as ideas, connections and perspectives that we’re not yet aware of or have forgotten. Their meaning is never clear until we’ve become aware of or remember them.
In the second case (ontology): collations that are fed ontological framing are necessarily bound to memory, initial conditions of some kind and/or association in terms of space, time, order, context, relation,… We build whole catalogs, dictionaries and theories about them! Triads, diads, quints, ontology charts, neural networks, semiotics and even the current research in linguistics are examples. Even if an ontology or set of them attempts to represent intrinsic meaning, it can only do so in a descriptive (extrinsic) way.
An ontology, no matter how sophisticated, is incapable of generating the purpose of even its own inception, not to mention the purpose of objects to which it corresponds! The knowledge is not coming from the data itself, it’s always coming from the observer of the data – even if that observer is an algorithm!
Therefore ontology-based semantic analysis can only produce the artifacts of knowledge, such as search results, association to other objects, ‘knowledge graphs’ like Cayley,.. Real knowledge precedes, transcends and includes our conceptions, cognitive processes, perception, communication, reasoning and is more than simply related to our capacity of acknowledgment. In fact knowledge cannot even be completely systematized, it can only be interacted with using ever increasing precision!
What is knowledge then?
• Knowledge is what awareness does.
• Awareness of some kind and at some level is the only prerequisite for knowledge and is the substrate upon which knowledge is generated.
• Awareness coalesces, interacts with and perpetuates itself in all of its form and function.
• Awareness which resonates (shares dynamics) at, near, or in some kind of harmony (even disharmony) with another tends to associate (disassociate) with that other in some way.
• These requisites of awareness hold true even for objects that are infinite or indeterminate.
• This is why knowledge, the meaning of meaning, can be precisely defined and even provides its own means for doing so.
• Knowledge is, pure and simply: the resonance, structure and dynamics of awareness as it creates and discovers for and of itself.
• Awareness precedes meaning and provides the only fundamentally necessary and sufficient basis for meaning of meaning expressing itself as knowledge.
• Knowledge is the dialog between participants in awareness – even if that dialog appears to be only one-way, incoherent or incomplete.
• Even language, mathematics, philosophy, symbolism, analogy, metaphor and sign systems can all be resolved to this common denominator found at the foundation of each and every one of them.
More information about the objects seen:
The objects on the surface of the pyramid correspond to basic structures denoting some of the basic paradigms that are being used to mine data into information and then collate that information into knowledge. You may notice that their basic structures do not change, only their content does. These paradigms are comprised of contra-positional fields that harmonize with each other so closely that they build complete harmonic structures. Their function is similar to what proteins and enzymes do in our cells.
This knowledge representation, which I made for a presentation in Nürnberg on April 29th, 2013, depicts a partial resonance domain with respect to sentient relation and orientation. The representation is not designed to be comprehensive in any respect, rather it is intended to help those who are not accustomed to viewing knowledge in this way.
It shows how, to quote Carl Sagan: “One voice in the Cosmic fugue.” can be represented more generally. There are certainly other possibilities with respect to relation and orientation between self and other.
You may need to stop this video occasionally as some of the transitions occur very quickly.I’ve indicated what is happening in the representation to aid in the interpretation of what is being shown.
I would appreciate any feedback you may want to give. I’d be pleased to answer any questions that may arise.
Thank you for watching!
A question has come in from an anonymous source asking for clarification on the use of “Resonance Domain”.
A resonance domain is comprised of resonance fields. When one or more fields are being examined, the group is called a domain.
For further clarity I’d like to add that we are looking at two separate fields (sentient beings) in terms of the relation they participate in and the orientation they share with each other.
Fields are always composed of other sub-fields and contain even partial fields from other sources!
UPDATE: “Why do you use the word ‘sentient’ in your description?”
There are basically three types of entities in our universe: 1) Having some measure of interiority (concept of self). 2) Artifacts 3) Heaps
‘Self and other’ can come in any number of combinations: The knowledge representation above requires that at least two of the participants are of type 1 (sentience) for this knowledge representation to hold.
Examples: Type 1) human and type 1) dog will work. Type 1) human and type 2) book will not work. Type 1) human and type 3) rock will not work.
This way of looking at our Kosmos (multiverse) is called Mathesis Universalis.
[PS: There are also the conditions that both participants are conscious of each other and that their shared consciousness is such that differences in semantics between them are ‘reconcilable’ (coherence). (for the scientists/mathematicians among us!)] 😉