Re: some information about anchor modeling

On Saturday, December 14, 2013 2:03:36 AM UTC+1, vldm10 wrote:
> Hi Derek,
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>
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> In this post I will briefly explain the main steps in my conceptual model.
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> As far as I know this is the first data model that was developed in full
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> compliance with Frege's theory. On the other hand, I think that this is the
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> first data model where the conceptual model is completely done. My data model
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> has the following elements of the conceptual model:
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>
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> 1.
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> My data model has a precise definition of the concept. I use Frege's definition
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> of the concept, which I improved so that Russell's Paradox is not valid in my
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> definition of the concept.
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> What is significant in this definition of the concept, it is the object which
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> Frege introduced and called the extension of the concept. Frege also defines
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> when two extensions are identical, which is fundamental. See my paper “Database
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> design and data model founded on concept and knowledge constructs”, sections 2
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> and 4.2.1. at http://www.dbdesign11.com
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> =======================
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> In my post from 23 October, 2013 in this thread I wrote: By accepting Frege's
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> definition about the extension, we can write the following:
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>
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> (i) Ǝx€xX
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> (ii) €xX & €yY = > (x = y < = > X ≡ Y)
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> It is possible to derive comprehension from (i) and extensionality from (ii),
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> for sets. Note that Russell’s paradox doesn’t hold in set theory which I apply.
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> ======================
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> Why am I writing about set theory? I write about it, because "E/ RM" and "RM",
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> use sets. These are for example "entity set" in E / RM and relation as a set of
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> n-tupples in RM. However, the E/ RM and RM do not say how they got sets at the
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> conceptual level.
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> Here is shown how from the concepts we are coming to the sets. In conclusion, we
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> can say that definition of concept is of fundamental character. So, my data
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> model is based on sets. More precisely, my data model consists of sets whose
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> elements represent states of entities or states of relationships.
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>
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> Note that Frege's principle of subordination applies here:
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> First comes the following relations: "an element which falls under the concept"
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> and the element is in the corresponding extension.
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> After that comes the relation: "an element belongs to a set" and this element is
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> a derivative of the corresponding element from the first level.
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> 2.
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> Now, let me try to explain “relationship” between concepts and predicates from
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> Frege’s theory. In my post from October 23, 2013, I schematically presented

Here I made a mistake. Instead of “In my post from October 23, 2013”, It should be the following text: In my post from September 24, 2013 (See the thread ”Sensible and NonsenSQL Aspects of the NoSQL Hoopla”)

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> Frege’s theory of predicates. The unsaturated expressions of the form S/NN…N
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> represent relations and entities from RM and E/RM.
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> ===========================
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> In Frege's theory, the predicates are language (grammatical) constructs which
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> denote concepts. Besides the denotation, Frege also developed theory of meaning,
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> thoughts and statements that contain actual knowledge as important components in
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> this relationship between predicates and concepts.
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> ===========================
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> This part of Frege's theory is very large and very significant. Therefore, this
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> matter can not be briefly outlined in the user group. Keep in mind, that in
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> Frege's work were started many other theories, such as the above-mentioned
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> unsaturated expression S / NN ... N, which are the beginning of the theory of
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> interpretation.
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> In his book, M. Dummett wrote: “ Frege would therefore have had within his grasp
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> the concepts necessary to frame the notation of the completeness of a
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> formalization of logic, as well as its soundness.”
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> With these few remarks about the importance of Frege's theory I want to
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> emphasize that the data model which is based on Frege's approach is good,
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> because it includes the foundations of mathematics.
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> 3.
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> At this point, I'll write about the identification. Identification I have
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> introduced as semantic procedure. So in my model, there are two semantic
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> procedures. In addition to concepts, there is identification.
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> In my paper “Database design and data model founded on concept and knowledge
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> constructs” section 5, at http://www.dbdesign11.com I wrote: The process of
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> identifying goes from a subject to the real world and this implies that the
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> subject has some knowledge about the entity which it tries to identify.
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> If we connect this with my definition about "Limitation of Interpretation", see
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> section 3 and formula (3.3.3) and the definitions of particular and universal
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> attributes (see section 3.3), then we come to several new conclusions.
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> For example, we conclude that we can only work with the attributes that are
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> known to us, ie which we can identify, directly or gradually. For example, we
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> can work only with the concepts of colors that we can identify. Note that when
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> working with the entity's attribute then we identify the particular attributes.
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> (see definition of particular attributes, I wrote in more details about these
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> terms in improved of my paper from 2009)
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> From this part of my text follows the solution of Russell's Paradox about which
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> I wrote in the thread “Does the phrase “Russell’s paradox” should be replaced
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> with another phrase?”
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> In my data model identification is realized by using an identifier. First, the
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> identification of attributes is defined. The attributes are identifiers, these
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> attributes I named universal attributes. Note that my data model, ie sets,
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> working with abstract objects. These abstract objects I denote with prefix m.
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> For example m-attributes, m-entities etc.. It is clear that the identification
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> occurs between the universal attributes and m-attributes. The procedure of
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> identification the attributes is described by formula (3.3.3), from my paper.
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>
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> The next level is entities. They are determined with the identifier of the
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> entity. The identifier of an entity determines all the entity's particular
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> attributes. Note also that the identifier of an entity enables the decomposition
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> of the entity into particular attributes, that is into the atomic structures.
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> The next level is the states of an entity. The states are determined with the
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> identifier of the corresponding state of the entity.
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> If knowledge about one state of an entity we named the particular knowledge,
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> then the corresponding identifier of a state enables the decomposition of the
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> particular knowledge into the atomic data structures.
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> 4.
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> This section is about meaning.
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> I will concentrate here only on the two aspects of a meaning, for that I think
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> that my work has given some contribution:
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> (i) Identification.
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> 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
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> quickly recall an entity that is stored in the memory.
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> However here, we do not have an entity; we have a state of an entity. A state of
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> an entity is very complex thing. As I wrote earlier in this thread, we store
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> complex objects in the memory, and we can get them from the memory, by combining
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> multiple identifiers. I have defined this rule as law of general character in
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> the work with the memory. In this way, I get the the meaning of complex
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> objects, such as the meaning of a state of an entity. In my data model, this is
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> achieved by applying the following two identifiers: the identifier of an entity
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> and the identifier of the state of the entity.
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> Similarly, the atomic structure (IdentifierOfEntity, Attribute1), is constructed
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> from two identifiers: IdentifierOfEntity and Attribute1 identifier. These two
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> identifiers provide the following meaning: the Attribute1 is a particular
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> attribute of the entity, ie this attribute belongs to this entity.
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> When I got the complete state, it means that I got complete actual knowledge
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> about the state of the entity in the real world. This state of the entity in the
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> real world is determined by the corresponding events. Of course, I can seek only
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> for the part of the knowledge about the state of an entity. In this case, I only
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> have the particular actual knowledge about the state of an entity.
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>
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> =================================
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> Totally or any particular knowledge about the state of an entity has significant
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> influence on the meaning of the entity. Note that particular knowledge about an
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> entity is very similar to what Frege called "sense" or "the mode of
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> presentation."
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> =================================
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> (ii) Links between truth, meaning and facts
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> I wrote about links between truth, meaning and facts in my paper “Semantic
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> databases and semantic machines”, section 1, 2, 3 at http://www.dbdesign11.com
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> -------------------------------
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>
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> So, roughly speaking the above-mentioned four sections in this post makes major
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> steps in my conceptual model. Section 1 provides a brief definition of the
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> concept, extensions and describes the transition from the concept to a set.
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> Section 2 briefly describes the relationship between predicates and concepts.
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> Section 3 describes the semantic identification procedure. Section 4 discusses
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> the construction of meaning for the entity.
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> The E/ RM model does not have any of these four sections. Therefore, in my
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> opinion E / RM is not the conceptual model. It has some intuitive elements of
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> semantics. In my opinion the best name for this model is the E / RM model,
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> without mixing with the conceptual modeling.
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> Please note that the conferences on conceptual modeling are run under the
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> leadership of men from E / RM with Honorary Chairman P. Chen. I think this
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> conference should have the name "entity relationships modeling".
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> Note also that E. Codd did not notice concepts, although the concepts are highly
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> associated with predicates.
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> In my opinion, conceptual modeling is important because it is the foundation for database theory.
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>
>
> Vladimir Odrljin
Received on Tue Dec 17 2013 - 01:16:11 CET