I was talking to someone today who is interested in: "literature about the difference between top layer ontologies, domain layer, application layer and data models?" I was wondering if anyone has any relevant papers. I told her that IMO data modeling isn't really what ontologies are for, that should be done with SHACL or UML. Interested in any papers or opinions.Michael
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IMHO, what makes something ontological is that it is aiming to picture what one thinks of as the real world explicitly. So if the data model is picturing the real world, it is in some sense ontological.
I realise that some people would say if it is in OWL (or somesuch) it is automatically an ontology - otherwise not.
However, we have built ontologies (pictures of the 'real' world) in UML, C++, JSON and XML. For us the base language is driven by the particular need at the time, it is the intended interpretation that (for us) makes it an ontology.
The ontological problems we wrestle with arise when we try to clearly represent in some computer (so formal in some sense) language the world.
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In what way is C++ not formal?
I'd be pretty surprised to find a C++ system that was a good match with the 'real' world.
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Wrt to Chris P’s point that Mike P repeats: “So if the data model is picturing the real world, it is in some sense ontological”
I agree with that. However, IMHO, If the model is a model of the “real world” then is it not a “data” model. I would rather call things what they are: if it’s a “data model” you’re modelling data structures. If you’re modelling the “real world” then it is not a “data” model – because it’s not a model of data. (The term “conceptual data model” should be banished from everyone’s vocabulary!) If you are modelling the real world, it could be called an “ontology”.
Mike: I’ve read David Hay’s books and they’re okay. He tends to get caught up in malady I call “meta-itis” – modeling the model in the model. (Mathew West, RIP, did this a lot too.)
So many, if not all, data modelling books talk about modelling “business objects” – “things important to the business.” That is - again IMHO and with all due respect – so lame! All of these books miss the important dimension of the why certain entities that are perceived to exist in the real work (i.e., “business objects”) are or should be represented in the model. It is not just that the “account, order, party, biological cell, atom, protein, ecosystem, planet” are there and are recognized and are therefore represented in the model. There is a business (usually process) requirement to capture information about these things. If you don’t have this connection, why is it in your model? That’s an issue I have with a lot of ontology work in the abstract – the ontologies just “represent objects in the real world” with no reason for why they’re in the model.
Bill Burkett
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Subject: [External] Re: [ontolog-forum] top layer ontologies, domain layer, application layer and data models?
This is a great discussion that gets to the heart of things IMO. Alex S. , thanks for using Claude to generate a list of useful readings; well, it's a good start, anyway. I read the book (s) by David C. Hay - all his books are excellent. Chris
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> John Sowa said at one time that logical data models contain ontology information because they relate entities?
I don’t disagree with that, Dr Sharma, but begs the question of what those terms mean. (It’s always back to “depends on what you mean by x”.)
B.
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John Sowa said at one time that logical data models contain ontology information because they relate entities? Thanks. Ravi (Dr. Ravi Sharma, Ph. D. USA) NASA Apollo Achievement Award Former Scientific Secretary iSRO HQ Ontolog Board of TrusteesParticle
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Michael,
WRT:Of course some new OWL users do that as well but if they define their axioms correctly the OWL reasoner will realize they are using the subclass relation incorrectly.
Let me point out the importance of scientific definitions to be used in our formal ontologies, i.e. science and theory first and formalization second.
In this particular case we are in Biology where the terms "mammal" and "dog" have definitions. And the challenge for OWL2 is if we can formalize these definitions in OWL2 as its expression power is not very good for real world modeling. But if these two definitions are formalized we are expecting to get a proof that dog implies mammal. If all terms have definitions then class hierarchy is just derived automatically.
We have shown this for definitions of igneous rocks here.
And formalization is a little bit of a restricted area: we took definitions of experts to formalize, but it's hard to resist giving your own definition. Therefore, each definition must contain a reference to the original source: a book or person - the author of the definition.
When we get different definitions inside the same theory, the task arises to prove equivalence or to present an entity which splits these definitions to check for authors their correctness.
Consider the same Biology area to define what it means to be a living being. And maybe one definition says bacteriophage is living but other says not.
From this scientific point of view C++ program is such a very special entity to prove its property of any kind. For example to satisfy specifications 😀
And for example, there is no general algorithm for proving a halting, so for each class of programs we will have to prove it separately.
WRT:One last point: just as with the concept "species" in biology all these definitions are useful fictions. It isn't like there is a clear boundary between what is design and what is analysis that everyone would agree on.
I don't think any biologist would agree with your "in biology all these definitions are useful fictions". Is that you can define better or from your point of view in biology can not be nonfiction definitions?
And we did not usually need "everyone would agree on", but inside of a working team every one must. The presentation of knowledge in the form of theory is absolutely necessary for the correct use of terms when they go beyond the naming of specific things and actions. Currently, precise definitions are what AI lacks. This is what they strive to replace with billions of examples.
Alex
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In addition, an inaccurate definition is better than its absence, since it can be clarified. I will also note that precise definitions are very necessary for robots, since it is better not to let robots into areas where we do not have them. Or you will have to let them in “at your own peril and risk.” What is happening now on a global scale.
Mike,
WRT:The SaaS project I'm working on uses relational data models that include these key entities or "business objects": account, order, party, biological cell, atom, protein, ecosystem, planet, sub-atomic, etc
It's better to say "objects of interest". And in any way is there a chance to point to a science or technologies from which these terms are? Just look in what form we have theoretical knowledge there.
Like this: account from GAAP, party - ???, biological cell, protein - biology, planet - astronomy; atom, subatomic - atomic physics, ecosystem - ???
What is an idea to keep all of these terms in one place? Encyclopedia?
What kind of knowledge processing do you keep in mind?
Maybe you should cooperate with Azamat Abdoullaev ontop...@gmail.com?
If something exists there always exists somebody who scientifically studies this kind of thing. i.e. she systematizes experimental knowledge about them 🏋️
Alex
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Hi, Alex,
1. Most system engineers do not know about the existence of TLO (upper ontology) and the difference of TLO and middle ontology and domain ontology. Moreover, they have significant problems in thinking about types, although they are better at this than “people from the street.” System engineers, of course, use ontics - middle ontology, the types of which are in no way connected through any TLO.
2. But those who manage system configuration (they are usual systems engineers) and are engaged in system description (multi-domain multi-model usually in some PLM database) are forced to think about ontology by their profession. There appears the problem of “working English” when you want to communicate with a Spaniard, a Chinese and a Malaysian, same is true for engineering CAD/CAM/CAE software that speaks it own language usually. Another thing is that at this moment all three are claiming “working English”, although the meeting is being held in English, which is already have all the words necessary for translation. In systems engineering, PLM people connects all sorts of CAD/CAM/CAE and other modeling tools to PLM databases (such as Siemens Teamcenter or Dassault Systemes 3DXperience). They know about TLO, but not knowing this name, they know it as a «neutral data model». E.g. such a model is ISO 15926, where TLO is in ISO 15926-2 part of the multi-part standard. This approach is failed, because ISO 15926-2 treated not as a «working English» but as a «very exotic Esperanto». Therefore TLO exists as a native data model to PLM systems, each vendor has its own variant that is far from academic notion of TLO.
3. The most advanced systems engineers, of course, think about TLO, and there they are already moving towards constructivism (as in constructive mathematics: not objects and relationships, but objects and construction operations). I have a review on this topic: https://ailev.livejournal.com/1653296.html (it in Russian, you can use Google Translate or similar service) - Mathew West helped organize all this. When John Sowa said that he was interested in what Mathew thought about this topic, then you can just go to the materials there and find out what he thinks. 15 years ago it was TLO HQDM (similar to ISO 15926-2). But a couple years ago Mathew sent me a link to these materials when I was interested in system approach applicable to descriptions, not only to 4D objects. English speakers can go strait to https://gateway.newton.ac.uk/event/tgmw80/programme and bypass may text in Russian. Matthew West said that this should not be seen as separate disparate talks, but starting with his own talk as six parts of one story. It's April 2021, so something else has probably been done there over the past year and a half.
4. We have experience in training system engineers. And our teachers come to the conclusion that it is useless to teach systems engineers if they are not first given knowledge of ontology and be fluent with types and relationships. Here are two recent reports on this topic (last weekend conference, sorry it in Russian)
-- Alexander Luchkov, “Teaching architectural practices at a university.”, https://www.youtube.com/watch?v=TdD7mKMEngA&t=8964s
-- Kirill Gaydamaka, “Experience in teaching system modeling at a university.”, https://www.youtube.com/watch?v=TdD7mKMEngA&t=11775s
In our System Management School (Aisystant) we teach students about TLOs, but I have already said in my Ontolog Forum talk (https://www.youtube.com/watch?v=G4S-Zrc5qUk&t=2s):
-- First we teach the types of objects and relationships: that they are important
-- Then we give the main ideas of 4D TLO ISO 15926 and point to the BORO book by Chris Partridge.
-- We teach to use multi-level multi-domain ontology in engineering projects: many levels of hierarchy in relation to classification at once. Let's say, here is just one of the courses where TLO (under two names: upper ontology and meta-meta-model, while we say that they are usually described in text, and not in formal language, and this is important!) actually learns how be used by engineers.
Best regards,
Anatoly
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What is an idea to keep all of these terms in one place? Encyclopedia?
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Chris –
> There are (at least) two senses of model - …
Models have purposes – models are developed for a reason(s). These purposes can be divided into two major kinds: (1) the domain task at hand – the application objective; and (2) the model intent or function. The first is easy to see and understand. The second is what you’ve seen with your “two senses”. Model Intent is the general, domain-independent function of the model and I think there are six senses:
These purposes overlap and Model Intent may shift in emphasis over time. In fact, most (data) models go through all of them except Predictive. The two senses you’ve identified are Specification/Control and Representation/Display (e.g., “scale model”)
I’ve been a systems engineer in the data modelling world for my whole career and I’m sensitive to what models can and can’t do. John Sowa recently quoted George Box (a statistician): “All models are wrong. Some are useful.” There’s another one I like and quote often from Alfred Korzybski that conveys a similar message: “The map is not the territory.”
Bill
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Thanks, Chris – I get your point and agree that the “first of these is a cleaner way to think about what we are doing”. I see the two steps as distinct and separate, however – entirely separate. They have different, though related, purposes. I acknowledge that the term “data modelling” often refers to tasks focused on creating a “model of a domain” and I think that that is a misnomer. Understanding and representing (in a model) the entities I see in a domain (that are important toward a purpose/goal of mine) is a task and an end in itself. Look at it as a “requirements statement” – a “Descriptive” or “Informative” model. The /data/ model is a separate model. I think you’d agree that many different and distinct (“Prescriptive”) models of data (actual data structures) can encode/represent/provide the same information about the entities modelled in the informative/descriptive model (with possibly differing degrees of fidelity). The prescriptive data model is a means; the informative/descriptive domain model is an end. Presumably, the informative/descriptive model will be less likely to change over time while data structures may change regularly. See Ron Ross’s “Business Knowledge Blueprints” for a published version of this idea.
Am I demonstrating an understanding of your point? Or am I still missing something?
Bill
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On a point of information, and to give credit where it is due, I believe the quote “All models are wrong, but some are useful”, is generally attributed to the statistician George Box.
Although, of course, the quote can apply to models in any field.
Regards,
Paul Warren
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com> On Behalf Of John F Sowa
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Subject: Re: [ontolog-forum] top layer ontologies, domain layer, application layer and data models?
I agree with Chris on the issues he discusses below.
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John,
We use theories to solve tasks. For example, in math we have the theory of undirected graphs. We use it to solve tasks to get one or another property of one or another graph, see Specific tasks of Ugraphia on a specific structure (in development, open for comments) The Russian version is here.
The first rule of thinking for physicists creating a model for a real system is What can we neglect? What of what we know about the physical system is not essential for solving our task?
The joke "All models are wrong, but some are useful." is from people who know how to create task oriented models for real systems.
How can we correctly pose a problem without having a theory? Every theory is about some object and processes. What kind of tasks and problems about objects and processes can we solve using this theory?
That is the question.
ABSTRACTION is a way of thinking we should pose before any abduction, deduction, induction.
Alex
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John,
I am now on the way to formalize Static theory as a part of Mechanics. And not only theory, where we have tractatus to begin with (Principia, and later) but also formulation and solution of tasks using the theory of static mechanical systems.
I will give an example of setting up a specific problem that has to be dealt with using concretization and abstraction. These knowledge transformations can probably be called differently.
initial problem statement:
“The weightless beam is held in a horizontal position by a hinged-fixed support in point A and a vertical rod BC.
At point D, a concentrated force F=30kN is applied to the beam at an angle of 50°."
concretization
“The weightless beam is held in a horizontal position by a hinged-fixed support at point A of the basic structure and a vertical rod BC, rigidly attached at point C to the basic structure.
At point D, a concentrated force F=30kN is applied to the beam at an angle of 50°."
HERE we had to add a basic structure and "rigidly attached…" to get a situation with a full composition of bodies.
more concretization and geometric abstraction
"points: A, B, C, D.
segments: A-D, B-C.
ray F: from point D in the direction 50° clockwise from A>D.
point B belongs to the segment A-D.
B-C is perpendicular to A-D in the left half-plane from A>D.
the ratio of segments A-B to B-D is 3/2."
where A>D means a ray from A in the direction of D.
HERE we get a geometric figure for the problem of finding the reaction of supports at points A and B will be solved.
I just wanted to share how concretization and abstraction are used when solving engineering problems using theoretical knowledge.
It is interesting that the texts of Euclid’s Elements that came to Europe did not contain drawings. I wonder if they were in Euclid's original treatise? Probably not.
I am looking forward to getting your terminology about this kind of knowledge processing.
We use theories to solve tasks with accuracy, precision needed for our goals.
We formalize theories and tasks to get support from a computer system.
The number of rules we apply during task solving is a little bit amazing.
Like this [1] from here. Please, keep in mind that it is under development.
Alex
[1]DATA
In what follows, for brevity, the word otext will denote the proposition or phrase being processed.
There are many ways to get from one text to another or several others. It is assumed that the processing of the original text is replaced by the processing of those obtained from it, and we know how to process the received ones, i.e. having received their values, get the value of the original one. We will call such processing methods rules.
The texts resulting from applying the rules are called subtasks.
The following are descriptions of different rules.
A rule named “subtask” only indicates that this subtask is separated into a separate task, i.e. must have its own solution block - if it exists, it is indicated in the “parameters” column (see below), and if the cell keeps "???", then the corresponding task has not yet been added to the framework! And strictly speaking, the task block has not been completed to the end.
This rule makes the transition from terms of theory to terms of structures.
For example, in "There exists x in U such that _e1 is incident to x." the term "incident" is used, introduced in the theories of Biria and used in Ugraphia for inc global variable. Knowing its “binding” with inc we can interpret the statement as “There exists x in U such that (_e1 x) in inc.”, where the term “incident” is not used.
It is important to emphasize that in the resulting statement no terms of theories are used!
This rule refers to the substitution of the definition of a term at the place of its use. Typically, a definition consists of a precondition formulated in a sentence beginning with the word “Let” and the definition itself, consisting of a phrase using the term, a syntactic connective (for example, “if and only if”) and a determinant - a phrase that specifies the meaning of the term. Definitions of terms are given in one theory or another - in our case it is Ugraphia. From a programming point of view, a definition is a macro command, and a “substitution” is a macro substitution, and sometimes the substituted text itself (preconditions and determinant) is modified.
The substitution comes down to the fact that the statements of the precondition form a linear block, and the determinant - a node of the decision tree.
For example, consider the statement
_e1 parallel to _e4.
it uses the term from Ugraphia - “parallel”.
Applying his definition:
we get two precondition statements:
_e1 is an edge.
_e4 is an edge.
which need to be checked for truth,
and a subtask:
_e1 and _e4 are different and _e1 has the same endpoints as _e4.
It is easy to see that the actual parameters of the place where the term is used: _e1 and _e4, are substituted in place of the formal parameters e1, e2 of the “macro command” of the definition.
A quantifier always runs over some set, sequence, and is expanded by running into an operation on statements or phrases written for each element of the set - a good move to finitism.
For example, consider the expansion of the second quantifier "every" in
"every member of every pair of inc belongs to U."
we need to run by inc and in our case, see the __inc block of the framework, we will get from inc for the first element:
"every member of (_e1 _v1) belongs to U."
etc. for each pair in inc.
Notes. The finitistic way for the quantifier expansion in mathematical logic can be found, for example, in Esenin-Volpin works. And it can be stated using an example like this
Let S be {e1 e2} and p() be a unary predicate on S. Then
"∀x:S p(x)" expands into "p(e1)∧p(e2)"
the generalization to the case of any finite number of elements in non-empty S is obvious.
Thus, the quantifier statement is expanded into several more specific statements and the meaning of the original statement is obtained by applying the operation ∧ or +, etc. to the expanded values.
This is a situation when in the original statement a conjunction such as “and” or the words “equals”, “plus” and the like are applied to two specific statements or phrases.
For example, in
"_e1 and _e4 are different and _e1 has the same endpoints as _e4."
we split along the second “and”, obtaining two statements - subtasks:
"_e1 and _e4 are different."
"_e1 has the same end vertices as _e4."
According to this rule, various free texts in NL are converted into equivalent but more regular ones.
For example,
"_e1 is incident to some element from U."
becomes
"there exists x in U such that _e1 is incident to x."
Execution of a rule consists of SEARCHING in a set and determining the presence or absence of an element that satisfies a given condition.
for example, applying the "choice" rule to a statement
" there is x in U such that (_e1 x) in inc. "
consists of running through U and checking for the current element that it, paired with _e1, is present in inc. If such an element is found, then the statement is considered true, otherwise - false.
From a certain set, a subset of elements is selected according to some criterion.
for example, in the phrase
" number of elements of U such that it's an edge and simple and incident _v1 "
before counting the quantity, it is necessary to obtain a subset of U with the described elements and then the phrase is converted into
" number of elements in list (_e1, _e2, _e4) ".
With text, the mental action of obtaining its meaning is performed.
For example, it is obvious that the following statements are true:
"_e1 and _e4 are different."
"_e1 is an element of U."
"(_e1 _v1) в inc."
although in the last two cases you need to look at U and inc, respectively.
The calculation formulated in the phrase is performed in the mind.
For example,
counting "number of elements in list (_e1, _e2, _e4)" will give the result 3.
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>> However, IMHO, If the model is a model of the “real world” then is it not a “data” model. I would rather call things what they are: if it’s a “data model” you’re modelling data structures. If you’re modelling the “real world” then it is not a “data” model – because it’s not a model of data. (The term “conceptual data model” should be banished from everyone’s vocabulary!) If you are modelling the real world, it could be called an “ontology”.
I like this… do others? Could it be considered as an accepted distinction? Canonical even?
Neil
From: 'Burkett, William [USA]' via ontolog-forum <ontolo...@googlegroups.com>
Sent: Saturday, April 27, 2024 12:40 AM
To: ontolo...@googlegroups.com
Subject: RE: [External] Re: [ontolog-forum] top layer ontologies, domain layer, application layer and data models?
Wrt to Chris P’s point that Mike P repeats: “So if the data model is picturing the real world, it is in some sense ontological”
I agree with that. However, IMHO, If the model is a model of the “real world” then is it not a “data” model. I would rather call things what they are: if it’s a “data model” you’re modelling data structures. If you’re modelling the “real world” then it is not a “data” model – because it’s not a model of data. (The term “conceptual data model” should be banished from everyone’s vocabulary!) If you are modelling the real world, it could be called an “ontology”.
Mike: I’ve read David Hay’s books and they’re okay. He tends to get caught up in malady I call “meta-itis” – modeling the model in the model. (Mathew West, RIP, did this a lot too.)
So many, if not all, data modelling books talk about modelling “business objects” – “things important to the business.” That is - again IMHO and with all due respect – so lame! All of these books miss the important dimension of the why certain entities that are perceived to exist in the real work (i.e., “business objects”) are or should be represented in the model. It is not just that the “account, order, party, biological cell, atom, protein, ecosystem, planet” are there and are recognized and are therefore represented in the model. There is a business (usually process) requirement to capture information about these things. If you don’t have this connection, why is it in your model? That’s an issue I have with a lot of ontology work in the abstract – the ontologies just “represent objects in the real world” with no reason for why they’re in the model.
Bill Burkett
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Subject: [External] Re: [ontolog-forum] top layer ontologies, domain layer, application layer and data models?
This is a great discussion that gets to the heart of things IMO. Alex S. , thanks for using Claude to generate a list of useful readings; well, it's a good start, anyway. I read the book (s) by David C. Hay - all his books are excellent. Chris
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>> However, IMHO, If the model is a model of the “real world” then is it not a “data” model. I would rather call things what they are: if it’s a “data model” you’re modelling data structures. If you’re modelling the “real world” then it is not a “data” model – because it’s not a model of data. (The term “conceptual data model” should be banished from everyone’s vocabulary!) If you are modelling the real world, it could be called an “ontology”.
I like this… do others? Could it be considered as an accepted distinction? Canonical even?
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On Tue, Jun 11, 2024 at 4:06 AM Neil McNaughton <nei...@oilit.com> wrote:
>> However, IMHO, If the model is a model of the “real world” then is it not a “data” model. I would rather call things what they are: if it’s a “data model” you’re modelling data structures. If you’re modelling the “real world” then it is not a “data” model – because it’s not a model of data. (The term “conceptual data model” should be banished from everyone’s vocabulary!) If you are modelling the real world, it could be called an “ontology”.
I like this… do others? Could it be considered as an accepted distinction? Canonical even?
You hit the nail on the head: https://philpapers.org/archive/SMIBCO
Barry
Thanks for this. It’s quite wordy though. Can I say that data is about databases and ontology is about graphs? Or maybe the other way around?
Neil
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com> On Behalf Of Barry Smith
Sent: Wednesday, June 12, 2024 1:08 AM
To: ontolo...@googlegroups.com
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On Tue, Jun 11, 2024 at 4:06 AM Neil McNaughton <nei...@oilit.com> wrote:
>> However, IMHO, If the model is a model of the “real world” then is it not a “data” model. I would rather call things what they are: if it’s a “data model” you’re modelling data structures. If you’re modelling the “real world” then it is not a “data” model – because it’s not a model of data. (The term “conceptual data model” should be banished from everyone’s vocabulary!) If you are modelling the real world, it could be called an “ontology”.
I like this… do others? Could it be considered as an accepted distinction? Canonical even?
You hit the nail on the head: https://philpapers.org/archive/SMIBCO
Barry
Thanks for this. It’s quite wordy though. Can I say that data is about databases and ontology is about graphs? Or maybe the other way around?
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Chris and I have indeed been having a long, offline conversation about this topic. I appreciate his patience and engagement as we’ve tried to understand each other. (I haven’t quite reached a full understanding of his position on “showing” versus “being a model about/of”, but I’m working on it – I suppose I should ready the Macbeth material. 😊)
A persistent point of non-agreement between our points of view is reflected in Chris’s statement:
“At some stage (the data modelling stage) one then uses exactly the same model as a data model to represent the data structure of the system you are building.”
It is entirely plausible and reasonable to do this, but I feel that a model of the real world (“concept model” or “ontology”) and a model of data (“data model”) are two separate models with distinct purposes (or perhaps distinct primary purposes.) The model of the real world should (ideally) be more robust and stable over time, presenting a “picture of the world as I/we understand it”. Many differently-structured models of data (and the data they govern) can provide the same information (with degrees of semantic approximation) as described/stated/asserted/represented by the model of the real world. Models of data (In addition to representing/encoding/conveying information about the real world) are also strongly and primarily driven by data processing considerations – requirements that do not (or should not) encumber models of the real world. The relationship between the model of the real world and one or more models of data is established by mapping. (The mapping effectively says: “these bits of data provide information about these things in the model of the real world.”)
Our positions are just different and our non-agreement is perfectly okay. Maybe we just haven’t yet uncovered our motivations or drivers for preferring our positions.
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John –
Your statement:
> In effect, it would be a model of models.
Would literally mean that an ontology is a “metamodel”. Does that mean every ontology is a metamodel? (I would think not.) And if an ontology is a model of a model, then it would be a “model of a model” and not a “model of the real world” unless there’s some transitivity going on that my ham-handed brain has some trouble wrapping itself around. I myself have trouble understanding models that model the model in the model (which in the past I’ve referred to as a malady called “meta-itis”.)
Bill
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com>
On Behalf Of John F Sowa
Sent: Tuesday, June 11, 2024 7:43 PM
To: ontolo...@googlegroups.com
Subject: Re: [External] Re: [ontolog-forum] top layer ontologies, domain layer, application layer and data models?
Isn't the "data" just an observation of an artifact or human, hence the data structure is the structure of the observation?
In other case, isn't the ontology how the model of reality is?
We already know the observation is mere observation however not the reality. However an ontology is the human understood reality hence a model of it. For example meta-physics cannot be observed hence a data model of it is not possible. However an ontology of the metaphysics is possible.
Alican
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On Jun 12, 2024, at 11:17 AM, Alican Tüzün <tuzun...@gmail.com> wrote:
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Random numbers are data. Are they measurements? What about the characters printed in a book version of a novel?
It seems to me relying on measurement is too restrictive. I presented this idea to the list several years ago, but Frank Farance and I arrived at a definition of datum as follows:
Designation (in the sense as described in ISO 704) of a value, where a value is a concept with a notion of equality defined.
One nice part of this definition in my mind is the lack of reliance on where data might come from.
Yours,
Dan
Dan Gillman
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From: ontolo...@googlegroups.com <ontolo...@googlegroups.com>
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@dan the definition you provided cuts off the observation-measurement-data chain, which in my opinion is more general definition than cybernetics grounded definition of data.
@nadin If you are talking about the measurement after the observation, we are talking about the same thing.
Best,
Alican
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>> However, IMHO, If the model is a model of the “real world” then is it not a “data” model. I would rather call things what they are: if it’s a “data model” you’re modelling data structures. If you’re modelling the “real world” then it is not a “data” model – because it’s not a model of data. (The term “conceptual data model” should be banished from everyone’s vocabulary!) If you are modelling the real world, it could be called an “ontology”.
I like this… do others? Could it be considered as an accepted distinction? Canonical even?
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Alican,
I just don’t see that observation (as the word is used say in statistics or science) and measurement are needed. How are my 2 examples either from measurements or not data?
The “cybernetics” definition takes each datum as it is and answers the question “what is this?” without caring where it came from. That is a separate issue and is covered under provenance considerations.
Each datum is a representation of some underlying meaning, hence a designation. The need for a notion of equality supports computation, for every data system involves moving data around, and this requires making copies. Copying has to be faithful.
In my experience data arise in many ways. Some of them come from secondary considerations: classification (creating categorical data from free text), recoding (creating categorical data from combinations of other values), allocation (using an algorithm to fill in a missing value), or imputation (using a statistical model to generate missing data). There are others. The more processing applied to some data, the less like the original they are. Do they always remain observations or measurements?
What about storing a value for Pi when needing to calculate the circumference of circles after measuring the diameters? Pi is a constant. It isn’t observed. It isn’t measured, either. So, the stored number is not a datum?
Moreover, when we generate a text file, say when we write a paper, we are creating characters. There’s no fundamental difference between the bytes needed to store the characters in a text document versus the characters in a file recording the results of a survey or scientific experiment. Even if we record the data using pencil and paper, the only real difference is how we organize the characters. And we can compute with both kinds.
One difference is the datatype, which tells us what operations we are allowed to use on some data. Statistical and scientific data look a lot different than text from that perspective. Datatypes apply to data, though.
I am very hard pressed to see that observation or measurement is an essential characteristic of data.
Yours,
Dan
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com>
On Behalf Of Alican Tüzün
Sent: Wednesday, June 12, 2024 3:33 PM
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Subject: [SOCIAL NETWORK] Re: [External] Re: [ontolog-forum] top layer ontologies, domain layer, application layer and data models?
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@dan the definition you provided cuts off the observation-measurement-data chain, which in my opinion is more general definition than cybernetics grounded definition of data.
@nadin If you are talking about the measurement after the observation, we are talking about the same thing.
Best,
Alican
On Wed, 12 Jun 2024, 9:26 pm 'Gillman, Daniel - BLS' via ontolog-forum, <ontolo...@googlegroups.com> wrote:
Random numbers are data. Are they measurements? What about the characters printed in a book version of a novel? How about storing an approximation of Pi so you can calculate the areas of circles you observed the radii for ? Pi isn'
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On Jun 12, 2024, at 3:53 PM, 'Gillman, Daniel - BLS' via ontolog-forum <ontolo...@googlegroups.com> wrote:
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MD> What does it mean to "model the real world"? IMO any model we build is "just a model"
You’ve got a good point here, Michael. A model is a model regardless of what’s modelled and the “real world” is everything, so a “model of the real world” is kind-of meaningless. And models do what you describe them to do.
The point is about what is modelled. Your example is a “model of the solar system”; the subject of the model is the solar system. So perhaps the original statement should be modified as “model a Universe of Discourse” to nominally circumscribe some portion of “the real world”.
MD> I also don't see the reason to ban "conceptual data model".
My argument is simple: the textbook and conventional understanding of the term “conceptual data model” is “a model of the objects important to the business”, or some such similar description. Where and how is the model, then, any kind of model of “data”? The whole intent (as I read the “conventional wisdom”) is to create a model that describes objects in a UoD that are important to a business process or software system. It is an information requirements specification rather than a system design specification.
Also, I’ve got this decidedly unconventional position that as soon as you “write down” a model in OWL, you’ve got a “data model” regardless of what you might want to call it. OWL has a formal structure and software processes consume and execute based on those structures – hence it is objectively “data model”. Whether is an OWL structure, nested XML elements, JSON objects, RDF, or relational table – they’re all data models because they model the structure of data elements in a collective whole.
Bill Burkett
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com>
On Behalf Of Michael DeBellis
Sent: Wednesday, June 12, 2024 4:10 PM
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Subject: Re: [External] Re: [ontolog-forum] top layer ontologies, domain layer, application layer and data models?
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MN> Data has no meaning. Read Shannon. Only referenced to it represents does it conjure meaning in the act of interpretation.
I agree with you on this, Mihai. But let’s include the other side: we speak, write, and create data with the intent of capturing and conveying “meaning” to an audience with the hope and intent that their interpretation process produces an approximately equivalent body of “meaning” (so we thereby “communicate”.)
Shannon was all about mathematics and mechanics. His use of the term “information” is devoid of “meaning” and should be thought of as “information capacity” (IMHO).
Bill
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Also, I’ve got this decidedly unconventional position that as soon as you “write down” a model in OWL, you’ve got a “data model” regardless of what you might want to call it. OWL has a formal structure and software processes consume and execute based on those structures – hence it is objectively “data model”. Whether is an OWL structure, nested XML elements, JSON objects, RDF, or relational table – they’re all data models because they model the structure of data elements in a collective whole.
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Reddit's AskScience forum is a good chance to pose questions to a group of noted scientists.
[ This is the title for the current askscience: ]
[ Next is the question I posed. My apologies for the long text, but I feel this is important territory and getting access to this group of professions is rare. I'm not sure which "Roger" responded, but we may be able to track him down if deemed sufficiently important. ]
It seems like the companies developing LLM systems are wandering about trying to figure out how to get to the next stage. Why haven't the Academies worked to build a roadmap forward? The E.U. has done that, and appear to be exceeding the U.S. in development collaboration, while the U.S. is promoting a competitive approach among the early startups. It seems when you want to build a town there is a Leather District and a Garment District; that seems to make more sense. Should we work to bring back an Office of Technology Assessment?
[ And finally we have the response from one moderator ]
•Ok, a caveat here - I (Roger) am biased. Which way will soon become obvious. It is possible another of our team will present a different facet in due course.
For the uninitiated (as I was until a little while ago) the acronym LLM in this context stands for Large Language Models – a form of artificial intelligence that can interact with humans in common language, compiling the bones from text from non-techie folk in order that a machine can process them on the one hand, and on the other hand, commute states/activities etc. into easy-to-understand text to deliver to non-technical folk (either end of course can be fitted with text/speech conversion).
First, the bad (as I see it)
Large Language Models have started to invade business help pages like a cancer. If one wanted help that was not available on the website, historically one would have initiated a web chat, sent an email or, heaven forbid, picked up the telephone. Such pleas would be handled by real people who had adequate knowledge of the company for whom they worked to triage, ensuring the enquiry received appropriate attention and if necessary refer/transfer to the right department or individual for onward processing. The up-side of this model is that customers and prospects felt – and were – looked after. The down side, because they were so good, people would use these routes instead of surfing/browsing/searching the company website – a victim of their own success if you like. The front-facing staff had to increase in number to meet demand, increasing business cost, reducing efficiency (but catching up with growth brought about by increasing clientele thanks to a good experience and proliferating recommendations).
Along comes LLM. Real human LLM sales people demonstrate them to those in the ivory towers, and present them as the bees’ knees for pointing people to the right places on the website, but do so in an ever-improving tone so that the uninitiated may not appreciate that the LLM isn’t human. Great – money saved by redundant front end staff, with the customer still feeling like and being looked after. But what if the enquiry needs information not available to the LLM or on the website? This is where the LLMs come unstuck. Instead of referring the matter to the correct human colleague, they are not programmed to deal with it. No matter how polite the LLM is and no matter what lexicon they articulate, the client feels – and is – given the run-around. And of course now most of the front end staff are gone, so if eventually you do need to get a hold of a human, at best it will be a long wait ("all our representatives are busy helping other customers...."), at worst you'll end up diverted to a call centre handling multiple companies with scripts from which they can't deviate - or get cut off.
Customer Service ratings have slowly but surely declined since the genesis and proliferation of LLMs. A HINT: Whenever you encounter one, keep typing or saying “Speak to human”. If it recognises it you’ll get back on a good road. If it doesn’t… find another company to deal with.
Now the good:
I'm grateful to my colleagues for this. LLMs are increasingly finding their way into companies to ease interfaces between humans and machines – Star Trek style. EG, “Please make the STL hole 4mm diameter” – with confirmation dialog may save having to redraft a CNC programming script.
For decades I have predicted a new computer language – English. Basically, with correct use of grammar, nouns and definitions, I think it should be possible to “write” a computer program in English and get predictable outcomes, with an LLM converting the prose to, say, C++. We’re actually getting there with things like Alexa and Google Home.
Now to the roadmap
For the customer services replacements, give them all to the nearest racoon and tell the racoons to run for the hills.
For the places where they really can improve things – man machine interfaces – yes let’s have a good roadmap. Perhaps the best-to-date LLM can compile one?
-John Bottoms
But most readers will never look at any dictionary.
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Hi John and other interested parties,
There was a thread on this forum a while back that ultimately lead to my publication of a presentation titled “Understanding Data” to slideshare, which also has a safer PDF variant on our website.
Basically, I defined data as observation in reusable form.
-- Regards, Kingsley Idehen Founder & CEO OpenLink Software Home Page: http://www.openlinksw.com Community Support: https://community.openlinksw.com Weblogs (Blogs): Company Blog: https://medium.com/openlink-software-blog Virtuoso Blog: https://medium.com/virtuoso-blog Data Access Drivers Blog: https://medium.com/openlink-odbc-jdbc-ado-net-data-access-drivers Personal Weblogs (Blogs): Medium Blog: https://medium.com/@kidehen Legacy Blogs: http://www.openlinksw.com/blog/~kidehen/ http://kidehen.blogspot.com Profile Pages: Pinterest: https://www.pinterest.com/kidehen/ Quora: https://www.quora.com/profile/Kingsley-Uyi-Idehen Twitter: https://twitter.com/kidehen Google+: https://plus.google.com/+KingsleyIdehen/about LinkedIn: http://www.linkedin.com/in/kidehen Web Identities (WebID): Personal: http://kingsley.idehen.net/public_home/kidehen/profile.ttl#i : http://id.myopenlink.net/DAV/home/KingsleyUyiIdehen/Public/kingsley.ttl#this
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Hi Michael,
Kingsley, that's a nice presentation.
Thanks.
Thanks for posting it. Are there any restrictions on using it?
No, just basic attribution as one would expect.
If I wanted to insert a few slides into my own presentation can I do that as long as I have proper attribution as to where the slides came from?
Yes, of course.
Assuming you are okay with people reusing parts of the presentation, is there any specific attribution you want us to include?
Just as you've already outlined :)
Kingsley
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Measurement: the dynamics of the measured affects the dynamics of the meter (this covers from measuring devices to sensorial perception and non-sensorial evaluations).
Rosen, R. 1978. Fundamentals of Measurement and representation of Natural Systems. New York :North-Holland
In this sense, observation is measuring.
The data is the outcome. The theory (based on thermodynamics) of how data is transmitted is Shannon’s contribution (with the unfortunate use of the word information as a substitute for data).
Mihai Nadin
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Hi Nadin,
Measurement: the dynamics of the measured affects the dynamics of the meter (this covers from measuring devices to sensorial perception and non-sensorial evaluations).
Rosen, R. 1978. Fundamentals of Measurement and representation of Natural Systems. New York :North-Holland
In this sense, observation is measuring.
The data is the outcome. The theory (based on thermodynamics) of how data is transmitted is Shannon’s contribution (with the unfortunate use of the word information as a substitute for data).
Mihai Nadin
Observation is basically existential quantification
(measurement). i.e., that something exists with certain
discernible attributes. Naturally, this is situation and observer
oriented.
Kingsley
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Unless anybody can suggest a better word for what is stored in the brain (or computer storage), I would say that 'information' is as good as any, and better than most.
In both protein synthesis and in later development, genes are symbols, in that there is no necessary connection between their form (sequence) and their effects. The sequence of a gene has been determined, by past natural selection, because of the effects it produces. In biology, the use of informational terms implies intentionality, in that both the form of the signal, and the response to it, have evolved by selection. Where an engineer sees design, a biologist sees natural selection.
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https://www.nadin.ws/wp-content/uploads/2011/09/computation_information_meaning.pdf
An article (vol. 2, Issue 1, January—April 2011, IJARITAC)—on computation, information, meaning.
Stored in computer memory—data
Brain: living matter interprets data continuously.
Information: data interpreted
Data:--of quantitative nature (see also the units of data, the measure)
Information: meaning (see also referencing data to what it means)
The genetic aspect—deserves a bit more nuance than in declaring genes to be symbols.
What is the meaning of labeling them as symbols? They are sequences of DNA—chemical compounds.
Mihai Nadin
From: ontolo...@googlegroups.com <ontolo...@googlegroups.com>
On Behalf Of Gary Berg-Cross
Sent: Monday, June 17, 2024 5:15 PM
To: ontolo...@googlegroups.com
Subject: Re: [ontolog-forum] top layer ontologies, domain layer, application layer and data models
>protein synthesis and in later development, genes are symbols,
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What is the meaning of labeling them as symbols? They are sequences of DNA—chemical compounds.
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Peirce made us aware of the ethics of terminology. Using labels without sharing in the meaning is an exercise in futility.
You can do computation in any medium. Read Feynman about this idea. DNA—you seem excited but not really well informed: 1994--Leonard Adleman performed the first ever truly molecular-level computation using DNA combined with the tools and techniques of molecular biology.
PLEASE: ontology without a clear understanding of the concept used degrades very fast. Even faster than the so-called AI….
Mihai Nadin
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Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine).[3] Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.[4]
Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data.[5] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge.[6] However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.[7][8]
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Hi Alex,
Hi Kingsley!
Nice presentation of your approach.
Let me just mention that nowadays we have so many kinds of data so intensively processed that we have Data Science [1]. The situation of 40 years ago when we had maybe 10 data models and 5 DBMS looks so simple now.For data in a broad sense we have this [2]. Where for me the key ideas are "value" and "structure".
Alex
"Data science is an interdisciplinary academic field[1] that uses statistics, scientific computing, scientific methods, processes, scientific visualization, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data.[2]Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine).[3] Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.[4]
Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data.[5] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge.[6] However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge.[7][8]
A data scientist is a professional who creates programming code and combines it with statistical knowledge to create insights from data.[9]"
"In common usage, data (/ˈdeɪtə/, also US: /ˈdætə/; ) are a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data are usually organized into structures such as tables that provide additional context and meaning, and which may themselves be used as data in larger structures."
Yes, but please note that I am very deliberate in the generic definition: Data is observation expressed in reusable form.
We observe entity relationship types (relations) in reusable form via notations used for expression and formats for serialization (regarding persistence). All of this is informed by logic as the organizing schema, meaning:
Everything is related to something else in a variety of observed or observable ways.
Kingsley
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Excellent:
Everything is related to something else in a variety of observed or observable ways.
I wish I can convince you all to read my new book:
https://link.springer.com/book/10.1007/978-3-031-43957-5
It argues for this idea.
Mihai Nadin
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It would be nice to discuss a little your definition of one of the meanings of the term "data".
So you: "Data is observation expressed in reusable form."
For me observation is a process. So, is it possible to say this way:
"Data is a result of observation expressed in reusable form."?
I like to work with definitions. Precise definitions are a gem of knowledge.
Alex
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Hi Alex,
Hi Kingsley,
It would be nice to discuss a little your definition of one of the meanings of the term "data".
So you: "Data is observation expressed in reusable form."
For me observation is a process. So, is it possible to say this way:
"Data is a result of observation expressed in reusable form."?
I like to work with definitions. Precise definitions are a gem of knowledge.
Alex
As John Sowa typically suggests, it’s always best to start from a good natural language dictionary. For instance, here’s a link that denotes the definition of observation, in English, from Merriam Webster.
https://www.merriam-webster.com/dictionary/observation#this
As you can see, the definition includes:
...
a: an act of recognizing and noting a fact or occurrence often involving measurement with instruments e.g., weather observations
b: a record or description so obtained e.g. Scientific observations were sent to the committee....
...
Kingsley
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Hi Alican,
I am just adding my part,
even though the observation is a process, the observer can "observe" only discretely. The expressed result of this discrete "observation" is the data. However, I did not understand the reusable form part.
Best,
Alican
Kingsley
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"Data is a result of observation expressed in reusable form."
"Data is a result of observation[m-w.a] expressed in reusable form."
"Data is observation[m-w.b] expressed in reusable form."
This is my observation of your definition :-)
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