Difference between revisions of "User:WikiSysop/Information Model"
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== 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 now needed to support cross-common domain requirements. | |||
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" is equal to "f" upon "x"), exactly maps to the widespread Input-Process-Output model permeating many methods; it is aptly descriptive to consider the Algebraic Formula the <u>epistemology</u> used to fashion many ontologies. | |||
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 in any depth, by anyone, clearly for the principle the ontologies being promulgated have no bearing upon the actual models being developed, and tested, by a data scientist most concerned with alternative theories of '''''interaction and evolution''''' within their domains. The core issue is no public domain ontology exists to structure the problems being solved by data scientists, meaning a scientist's use of today's public ontologies can be expected to be little more than performing a public service of dubious value. | |||
The path forward employs the epsilon operator in the generic formula, <i>f(x) ε y</i> (where "y" <u>emerges from</> "f" upon "x"), to identify Emergence Paths pertinent to an Individual or class of Individuals. An Emergence Epistemology is detailed for development of models directly pertinent to ontologists and data scientists alike. | |||
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The following material is being rewritten | |||
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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. | 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. | ||
Revision as of 21:56, 11 January 2023
The Best Practices Wiki Ontology
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 now needed to support cross-common domain requirements.
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 permeating many methods; it is aptly descriptive to consider the Algebraic Formula the epistemology used to fashion many ontologies.
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 in any depth, by anyone, clearly for the principle the ontologies being promulgated have no bearing upon the actual models being developed, and tested, by a data scientist most concerned with alternative theories of interaction and evolution within their domains. The core issue is no public domain ontology exists to structure the problems being solved by data scientists, meaning a scientist's use of today's public ontologies can be expected to be little more than performing a public service of dubious value.
The path forward employs the epsilon operator in the generic formula, f(x) ε y (where "y" emerges from</> "f" upon "x"), to identify Emergence Paths pertinent to an Individual or class of Individuals. An Emergence Epistemology is detailed for development of models directly pertinent to ontologists and data scientists alike.
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).
Best Practices Wiki Ontology
User:WikiSysop/Information Model/rdf