Re: some information about anchor modeling
Date: Fri, 13 Dec 2013 17:03:36 -0800 (PST)
Message-ID: <2f4d7797-38d3-47ca-aced-20e06907fb83_at_googlegroups.com>
Hi Derek,
In this post I will briefly explain the main steps in my conceptual model.
As far as I know this is the first data model that was developed in full
compliance with Frege's theory. On the other hand, I think that this is the
first data model where the conceptual model is completely done. My data model
has the following elements of the conceptual model:
1.
What is significant in this definition of the concept, it is the object which
Frege introduced and called the extension of the concept. Frege also defines
when two extensions are identical, which is fundamental. See my paper “Database
design and data model founded on concept and knowledge constructs”, sections 2
and 4.2.1. at http://www.dbdesign11.com
It is possible to derive comprehension from (i) and extensionality from (ii),
for sets. Note that Russell’s paradox doesn’t hold in set theory which I apply.
Why am I writing about set theory? I write about it, because "E/ RM" and "RM",
use sets. These are for example "entity set" in E / RM and relation as a set of
n-tupples in RM. However, the E/ RM and RM do not say how they got sets at the
conceptual level.
Note that Frege's principle of subordination applies here:
First comes the following relations: "an element which falls under the concept"
and the element is in the corresponding extension.
After that comes the relation: "an element belongs to a set" and this element is
a derivative of the corresponding element from the first level.
2.
This part of Frege's theory is very large and very significant. Therefore, this
matter can not be briefly outlined in the user group. Keep in mind, that in
Frege's work were started many other theories, such as the above-mentioned
unsaturated expression S / NN ... N, which are the beginning of the theory of
interpretation.
In his book, M. Dummett wrote: “ Frege would therefore have had within his grasp
the concepts necessary to frame the notation of the completeness of a
formalization of logic, as well as its soundness.”
With these few remarks about the importance of Frege's theory I want to
emphasize that the data model which is based on Frege's approach is good,
because it includes the foundations of mathematics.
3.
In my paper “Database design and data model founded on concept and knowledge
constructs” section 5, at http://www.dbdesign11.com I wrote: The process of
identifying goes from a subject to the real world and this implies that the
subject has some knowledge about the entity which it tries to identify.
If we connect this with my definition about "Limitation of Interpretation", see
section 3 and formula (3.3.3) and the definitions of particular and universal
attributes (see section 3.3), then we come to several new conclusions.
For example, we conclude that we can only work with the attributes that are
known to us, ie which we can identify, directly or gradually. For example, we
can work only with the concepts of colors that we can identify. Note that when
working with the entity's attribute then we identify the particular attributes.
From this part of my text follows the solution of Russell's Paradox about which
I wrote in the thread “Does the phrase “Russell’s paradox” should be replaced
with another phrase?”
In my data model identification is realized by using an identifier. First, the
identification of attributes is defined. The attributes are identifiers, these
attributes I named universal attributes. Note that my data model, ie sets,
working with abstract objects. These abstract objects I denote with prefix m.
For example m-attributes, m-entities etc.. It is clear that the identification
occurs between the universal attributes and m-attributes. The procedure of
identification the attributes is described by formula (3.3.3), from my paper.
The next level is entities. They are determined with the identifier of the
entity. The identifier of an entity determines all the entity's particular
attributes. Note also that the identifier of an entity enables the decomposition
of the entity into particular attributes, that is into the atomic structures.
The next level is the states of an entity. The states are determined with the
identifier of the corresponding state of the entity.
If knowledge about one state of an entity we named the particular knowledge,
then the corresponding identifier of a state enables the decomposition of the
particular knowledge into the atomic data structures.
4.
(i) Identification.
However here, we do not have an entity; we have a state of an entity. A state of
an entity is very complex thing. As I wrote earlier in this thread, we store
complex objects in the memory, and we can get them from the memory, by combining
multiple identifiers. I have defined this rule as law of general character in
the work with the memory. In this way, I get the the meaning of complex
objects, such as the meaning of a state of an entity. In my data model, this is
achieved by applying the following two identifiers: the identifier of an entity
and the identifier of the state of the entity.
Similarly, the atomic structure (IdentifierOfEntity, Attribute1), is constructed
from two identifiers: IdentifierOfEntity and Attribute1 identifier. These two
identifiers provide the following meaning: the Attribute1 is a particular
attribute of the entity, ie this attribute belongs to this entity.
When I got the complete state, it means that I got complete actual knowledge
about the state of the entity in the real world. This state of the entity in the
real world is determined by the corresponding events. Of course, I can seek only
for the part of the knowledge about the state of an entity. In this case, I only
have the particular actual knowledge about the state of an entity.
(ii) Links between truth, meaning and facts
So, roughly speaking the above-mentioned four sections in this post makes major
steps in my conceptual model. Section 1 provides a brief definition of the
concept, extensions and describes the transition from the concept to a set.
Section 2 briefly describes the relationship between predicates and concepts.
Section 3 describes the semantic identification procedure. Section 4 discusses
the construction of meaning for the entity.
The E/ RM model does not have any of these four sections. Therefore, in my
opinion E / RM is not the conceptual model. It has some intuitive elements of
semantics. In my opinion the best name for this model is the E / RM model,
without mixing with the conceptual modeling.
Please note that the conferences on conceptual modeling are run under the
leadership of men from E / RM with Honorary Chairman P. Chen. I think this
conference should have the name "entity relationships modeling".
Note also that E. Codd did not notice concepts, although the concepts are highly
associated with predicates.
In my opinion, conceptual modeling is important because it is the foundation for database theory.
Vladimir Odrljin
Received on Sat Dec 14 2013 - 02:03:36 CET
My data model has a precise definition of the concept. I use Frege's definition
of the concept, which I improved so that Russell's Paradox is not valid in my
definition of the concept.
In my post from 23 October, 2013 in this thread I wrote: By accepting Frege's
definition about the extension, we can write the following:
(i) Ǝx€xX
(ii) €xX & €yY = > (x = y < = > X ≡ Y)
Here is shown how from the concepts we are coming to the sets. In conclusion, we
can say that definition of concept is of fundamental character. So, my data
model is based on sets. More precisely, my data model consists of sets whose
elements represent states of entities or states of relationships.
Now, let me try to explain “relationship” between concepts and predicates from
Frege’s theory. In my post from October 23, 2013, I schematically presented
Frege’s theory of predicates. The unsaturated expressions of the form S/NN…N
represent relations and entities from RM and E/RM.
In Frege's theory, the predicates are language (grammatical) constructs which
denote concepts. Besides the denotation, Frege also developed theory of meaning,
thoughts and statements that contain actual knowledge as important components in
this relationship between predicates and concepts.
(see definition of particular attributes, I wrote in more details about these
terms in improved of my paper from 2009)
This section is about meaning.
I will concentrate here only on the two aspects of a meaning, for that I think
that my work has given some contribution:
Identification of certain entity helps us to have quickly access to the entity that is stored in memory. We can say it, in this way: Identifying helps us, to
quickly recall an entity that is stored in the memory.
Totally or any particular knowledge about the state of an entity has significant
influence on the meaning of the entity. Note that particular knowledge about an
entity is very similar to what Frege called "sense" or "the mode of
presentation."
I wrote about links between truth, meaning and facts in my paper “Semantic
databases and semantic machines”, section 1, 2, 3 at http://www.dbdesign11.com