Difference between revisions of "User:WikiSysop/Information Model"
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=The Best Practices Wiki Ontology = | =The Best Practices Wiki Ontology = | ||
<blockquote> | |||
The ''scientific method'' is a systematic approach to acquiring knowledge and understanding about the natural world. The epistemology that underpins the scientific method is ''empiricism'', which is the idea that knowledge comes from observation and experimentation. This means that scientific knowledge is based on ''evidence'' that can be observed, measured, and tested, and that scientific ''theories'' and ''hypotheses'' must be testable and falsifiable in order to be considered valid. The scientific method is also characterized by the ''principles'' of skepticism and critical thinking, which require that scientists question existing beliefs and carefully evaluate new evidence in order to arrive at well-supported conclusions. | |||
</blockquote> | |||
<div style='font:11pt fixed;line-height:120%'> | <div style='font:11pt fixed;line-height:120%'> | ||
== An Emergence Epistemology for Modern Ontologies == | == An Emergence Epistemology for Modern Ontologies == | ||
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===Motivation=== | ===Motivation=== | ||
Presently ontologies are built using approaches first typified by the Formula Translating System (FORTRAN), and are thus based on essential notions of "data structures" as proxies for operands to an algebraic formula; "routines" being a particular functional formula; and "return values" being the specific knowable output of the formula. The generic formula, <i>y = f(x)</i> (where "y" <u>is equal to</u> "f" upon "x") exactly maps to the widespread Input-Process-Output model underpinning many methods; it is | Presently ontologies are built using approaches first typified by the Formula Translating System (FORTRAN), and are thus based on essential notions of "data structures" as proxies for operands to an algebraic formula; "routines" being a particular functional formula; and "return values" being the specific knowable output of the formula. The generic formula, <i>y = f(x)</i> (where "y" <u>is equal to</u> "f" upon "x") exactly maps to the widespread Input-Process-Output model underpinning many methods; it is apt to consider this Algebraic Formula an epistemology itself as its method of organization of inputs and outputs (as instances of an equivalence relation) is strictly domain-neutral. | ||
However if success of ontologies is judged by their fruits, the Formula paradigm appears to have produced representations of reality that are not being used to | However if success of ontologies is judged by their fruits, the Formula paradigm appears to have produced representations of reality that are not being used to an overwhelming degree, by anyone, for the principle reason the ontologies being promulgated as "common and global and domain-neutral" have no bearing on the models being developed and tested by a data scientist most concerned with theories of '''''interaction and evolution''''' within their chosen domains. The core problem is no public ontology exists to structure the problem-solving space needed by data scientists, thereby reducing use of ontologies to that of a public gesture. | ||
Formula data structures first emerged as Database Systems and then as Triple Stores. Triple Stores consolidated gargantuan systems of DBMS tables, using ontologies as a dictionary of <u>existential</u> relations between keys inherited from those tables, in theory to reduce processing costs while increasing processing opportunities. | |||
The Resource Description Framework (RDF) introduced a model for an | The Resource Description Framework (RDF) introduced a model for an ''Axiom'' (expressed in any of several RDF Notations) with its ''rdf:Statement'' construct, a "triple" composed of three pieces, {<i>rdf:Subject, rdf:Predicate, rdf:Object</i>}, essential to logic processors. RDF also addressed list processors, defining without further comment, the <i>rdf:Pair = {rdf:First, rdf:Rest</i>} construct. The most fundamental RDF concept is ''rdf:Resource'' an identifiable but otherwise void concept that is the basis for ''rdf:Individual'' to which property values are attached (with each property name defined by an instance of ''rdf:Property''). A categorical "class" is also associated with every Individual when it is instantiated (with each class name defined by an instance of ''rdfs:Class'''). | ||
Finally it is noted the Standard <i>Model-View-Controller</i> Model taught to computer science students is a "triple" relation too, catering to a growing orientation towards database models, process models and ontology models. | |||
== Epsilon Operator == | |||
<div style='float:left;font-weight:bold;size:big;margin:auto;text-align:center;padding:1em;margin:0 2em 0 2em;border:black solid 2px'><i>y Э f(x)</i>, or, <br/>(where "f" upon "x" <u>emerges from</u> "y")<hr/><i>f(x) ε y</i> <br/>(where "y" <u>emerges from</u> "f" upon "x")</div> | <div style='float:left;font-weight:bold;size:big;margin:auto;text-align:center;padding:1em;margin:0 2em 0 2em;border:black solid 2px'><i>y Э f(x)</i>, or, <br/>(where "f" upon "x" <u>emerges from</u> "y")<hr/><i>f(x) ε y</i> <br/>(where "y" <u>emerges from</u> "f" upon "x")</div> | ||
<div style='width:90%'>In this context an '''''Emergence Epistemology''''' introduces two operators to specify '''Emergence Paths''' for entities and properties modelled by an ontology. By virtue of basic algebraic associativity, backward and forward operators are used interchangeably.</div> | <div style='width:90%'>In this context an '''''Emergence Epistemology''''' introduces two operators to specify '''Emergence Paths''' for entities and properties modelled by an ontology. By virtue of basic algebraic associativity, backward and forward operators are used interchangeably.</div> | ||
Revision as of 15:28, 12 January 2023
The Best Practices Wiki Ontology
The scientific method is a systematic approach to acquiring knowledge and understanding about the natural world. The epistemology that underpins the scientific method is empiricism, which is the idea that knowledge comes from observation and experimentation. This means that scientific knowledge is based on evidence that can be observed, measured, and tested, and that scientific theories and hypotheses must be testable and falsifiable in order to be considered valid. The scientific method is also characterized by the principles of skepticism and critical thinking, which require that scientists question existing beliefs and carefully evaluate new evidence in order to arrive at well-supported conclusions.
An Emergence Epistemology for Modern Ontologies
On the heels of recent evolution in mathematics and astronomy, physics, biology, social sciences, and cognitive research, a new approach to the process of information modeling is needed to support widely common cross-domain requirements, that is, this article proposes a candidate Standard Ontology Model (SOM).
Motivation
Presently ontologies are built using approaches first typified by the Formula Translating System (FORTRAN), and are thus based on essential notions of "data structures" as proxies for operands to an algebraic formula; "routines" being a particular functional formula; and "return values" being the specific knowable output of the formula. The generic formula, y = f(x) (where "y" is equal to "f" upon "x") exactly maps to the widespread Input-Process-Output model underpinning many methods; it is apt to consider this Algebraic Formula an epistemology itself as its method of organization of inputs and outputs (as instances of an equivalence relation) is strictly domain-neutral.
However if success of ontologies is judged by their fruits, the Formula paradigm appears to have produced representations of reality that are not being used to an overwhelming degree, by anyone, for the principle reason the ontologies being promulgated as "common and global and domain-neutral" have no bearing on the models being developed and tested by a data scientist most concerned with theories of interaction and evolution within their chosen domains. The core problem is no public ontology exists to structure the problem-solving space needed by data scientists, thereby reducing use of ontologies to that of a public gesture.
Formula data structures first emerged as Database Systems and then as Triple Stores. Triple Stores consolidated gargantuan systems of DBMS tables, using ontologies as a dictionary of existential relations between keys inherited from those tables, in theory to reduce processing costs while increasing processing opportunities.
The Resource Description Framework (RDF) introduced a model for an Axiom (expressed in any of several RDF Notations) with its rdf:Statement construct, a "triple" composed of three pieces, {rdf:Subject, rdf:Predicate, rdf:Object}, essential to logic processors. RDF also addressed list processors, defining without further comment, the rdf:Pair = {rdf:First, rdf:Rest} construct. The most fundamental RDF concept is rdf:Resource an identifiable but otherwise void concept that is the basis for rdf:Individual to which property values are attached (with each property name defined by an instance of rdf:Property). A categorical "class" is also associated with every Individual when it is instantiated (with each class name defined by an instance of rdfs:Class').
Finally it is noted the Standard Model-View-Controller Model taught to computer science students is a "triple" relation too, catering to a growing orientation towards database models, process models and ontology models.
Epsilon Operator
(where "f" upon "x" emerges from "y")
f(x) ε y
(where "y" emerges from "f" upon "x")
Referents and References
In semantics and philosophy, a referent is used to establish a connection between a word, phrase, or symbol, and the thing or object it represents or denotes. This allows for clear communication and understanding of the meaning of language. In programming, referent is not commonly used, but it can be used in similar way to establish a connection between a variable or identifier and the object it references.
This epistemology claims all ontologies must first distinguish their expressions as being references to things that exist, or as the things that exist themselves. All ontologies must define "Referent" and "Reference" as top-level classes of expression otherwise persistent semantic confusion risks the support of a user base that cannot perceive any fundamental organization of the ontology.
Implication. Because language, symbols and numbers are used to name things, any Name, Symbol or Identifier is a reference to the concept being described. Referents thus emerge from References, and Referents are thus preceded by instantiation of its Reference. A principle of Emergence applies in this manner: A Reference may exist in Time without a Referent, emerging subsequent as "completed" once the Referent is associated with its Reference. Alternatively, a Reference has not emerged and is "Incomplete", until its association with a Referent.
Implication. Because Referents may be maintained in or referenced by a Catalog classified per a Referent Class, ontologies are required to support these axioms and forms.
1.1 Reference Є Referent Є Catalog
1.2 Reference Є Referent Є Catalog_Reference Є Catalog
1.3 Catalog Э Referent Э Reference
1.4 Catalog Є Referent Э Reference
In 1.1, a specific process for instantiation is indicated for any particular Referent: a particular Reference is instantiated and a non-specific Catalog is thereafter instantiated. A Reference_Catalog is distinguished from a Referent_Catalog by its structure, that is, whether the Catalog contains References or contains Referents; in the latter case, one would expect the Catalog to be a database or serialized data-set, e.g., a Shelf of Books, while all Referent "indexes" are perforce Reference Catalogs, e.g., a each Catalog entry is a References to some individual, set or group of Books.
In 1.2, a specific Catalog is to be created for any Referent_Class
The following material is being rewritten
The Best Practices Wiki Information Model is fundamental to identify (a) semantic tags to markup Best Practices essays (b) semantic classes for Practice, Policy, Procedure and Process models, of prime interest (c) classes for Act, Action, Activity and Task that are the detailed enumerated steps for a "method" that achieves a particularly useful objective.
Related Open Source
mw:Extension:SemanticTasks: provides email task notifications and reminders whose model is centered on assignment of Users to Tasks.
 | Amina Annane, Nathalie Aussenac-Gilles, Mouna Kamel. BBO: BPMN 2.0 Based Ontology for Business Process Representation. 20th European Conference on Knowledge Management (ECKM 2019), Sep 2019, Lisbonne, Portugal.  |
References
- [https:\\kissflow.com/workflow/bpm/business-process/ The Extensive Guide to Business Processes]
- ERP Readiness Series: The Four Core Processes Every Business Should Document
Referents
Partitive Classes
Taxonomic Classes
Topic Annotation (Tags)
Linguistic Classes
Instance Referents
{{Category:Tag <br/>|rdfs:subclassOf=Reference <br/>|name= <br/>|id= <br/>|label= <br/>|path= <br/>|timestamp= <br/>|@category:name }} <!-- {{Hierarchy|Start|2|skos:Concept|https://www.w3.org/2004/02/skos/}} {{Hierarchy|End|2|Gender|skos:Concept}} -->
Determiners include articles (a, an, the), cardinal numbers (one, two, three...) and ordinal numbers (first, second, third...), demonstratives (this, that, these, those), partitives (some of, piece of, and others), quantifiers (most, all, and others), difference words (other, another), and possessive determiners (my, your, his, her, its, our, their).
