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From: Paola Di Maio <paoladimaio10@gmail.com>
Date: Tue, 26 May 2020 07:26:38 +0800
Message-ID: <CAMXe=SqisC99CwxhZKShXHr_OggwT6avvk9iF9Wz33NVvX5tsA@mail.gmail.com>
Cc: ProjectParadigm-ICT-Program <metadataportals@yahoo.com>, W3C AIKR CG <public-aikr@w3.org>
Either way it would be interesting to see -
what kind of use could it have in what context, etc

we cannot realistically evaluate a property which has not yet been


On Tue, May 26, 2020 at 12:48 AM carl mattocks <carlmattocks@gmail.com>

> Milton
> Thanks for critique. I am in total agreement about the complexity of the
> challenge. I also agree that using
> information ( and Data) science methods are essential... Especially if
> augmented by AI Strategist plans.
> Carl
> On Mon, May 25, 2020, 12:02 PM ProjectParadigm-ICT-Program <
> metadataportals@yahoo.com> wrote:
>> I would like to DISAGREE on the UNIQUENESS of a KRID. Whereas in linked
>> data and semantic web technologies we can create UNIQUE identifiers for
>> data, this is not the case for knowledge. In our modern world thousands of
>> scientific domains of discourse exist, and even agreeing upon uniquely
>> naming each of these can get us into trouble.
>> There will always be ambiguity in knowledge, both in the domains of
>> discourse or instances or identified objects therein. The closest we will
>> get is unique coding systems as used in library information systems.
>> The problem is natural language, its richness allows us multiple ways to
>> exactly describe the same things, and just like there are synonyms in
>> natural language, there are competing terminologies in similar but slightly
>> different domains of discourse.
>> Milton Ponson
>> GSM: +297 747 8280
>> PO Box 1154, Oranjestad
>> Aruba, Dutch Caribbean
>> Project Paradigm: Bringing the ICT tools for sustainable development to
>> all stakeholders worldwide through collaborative research on applied
>> mathematics, advanced modeling, software and standards development
>> On Saturday, May 23, 2020, 10:21:00 PM ADT, Paola Di Maio <
>> paola.dimaio@gmail.com> wrote:
>> Paul
>> A Knowledge Resource IDentifier is the unique name for a particular
>> Instance of discourse.
>> who says that? where is this stated?  to what discourse does this apply?
>> what type of instance, in which context?
>> It thus becomes a reference point for any discussion of that instance. If
>> this KRID is stable then it becomes THE reference point for ALL discussions
>> of that instance.
>> very premature to talk about stability, when this identified has not yet
>> been defined sufficiently to be able to talk about it
>> I cannot comment on the rest of your email until I see the taxonomy for
>> please start the definition of KRID in a document, its purpose and
>> explain what kind of values would be there,
>> so that we can understand what you are talking about?
>> Thanks!!
>> In an XML report knowledge is gathered into its repository (roughly a
>> storage array)
>> Information like:
>>    -  Operational information -  its position in the report
>>    - And some meta data like tag-name, attributes and values, value
>>    after the tag, peer resolution, etc.
>> All of this information needs to be collected under an instance
>> identifier. The KRID developed for an XML report is by nature unstable. It
>> only exists for the moment of the XML.
>> BUT we can find a stable KRID IF add more information to our repository
>> by including the XSD’s knowledge. Like a definition identifier and its
>> text. And it’s this definition identifier as a KRID that is  stable across
>> ALL XML reports in this XSD’s format.
>> One of the Goals of The AIKR group is to:
>>  Discover, name and define the usage of XSD information items that can be
>> added to XML repositories. In AI (machine learning or neural networks) this
>> KRID names the input/information feed CONSISTENTLY for ALL XML reports in
>> this XSD format.
>> Other information comes from the XSD that can be applied to the XML
>> report. Like
>> -  a format identifier and its text,
>> - The provenance (history or lineage) of this “Tag”,  the parsing
>> workflow.
>> format : is this date month/day/year? Or year/month/day? Is this 24 hour
>> time? Or AM/PM?
>> Provenance: that a “Goal” has a string format is useful but answering
>> “Who’s goal under WHAT circumstances” is important too. (see also below for
>> more). NOTE: To provide provenance AIKR processes  MUST follow the rules
>> laid out by our standards .
>> - The Parsing workflow: with the advent of “wizards” the validation has
>> been “worked out” (well until the next version of the XSD comes out) .
>> “worked out” also means that Validation is reduced to a developer's
>> afterthought. But with KRID based repositories we can perform extra-session
>> analysis across XML reports.
>> like:
>> Rooting: reducing words to their roots by removing the prefix and suffix.
>> “Precasts”  becomes ‘cast’. Also capitalization is reduced to lower case.
>> This produces a set of search contenders.
>> Acronymization:
>>    - coring: cores like the dublin core have definitions for known
>>    phrases like :
>> “Author of” or
>> “Date of publication”
>>    - Industrial cores: all industries have their jargon (a tennis court
>>    is very different from a municipal court , “playing ball” means different
>>    things )
>> Categorization: by usage of different cores for acronymization industrial
>> categories can be registered.
>> Patterns: phone numbers have a unique pattern, as do zip codes, URI’s and
>> filenames. All of these add to our knowledge.
>> Framing and frame completion:
>> Framing simply is the discovery of the largest domain under a reduced
>> tag.
>> A “reduced tag” is an endpoint  in a provenance chain.
>> This point can have several value choices, the collection of which is
>> called its domain.
>> From one XML to the next XML the largest set can be collected. Thereafter
>> this set can be offered to the XML creator as a possible set of options. OR
>> offered to the XML report creator as an option for expansion or improvement.
>> definitions:
>> In data science we acknowledge:
>> Dominion:  the “table” in an SQL Database is the dominion of discourse.
>> In object class definition we acknowledge the class to be the dominion.
>> Attribute: column names in an SQL database.
>> Peers: a set of attributes associated with a dominion.
>> Attribute;value pair: an attribute AND its value (one of its Domain
>> values)
>> Dominion instance or instance: An instance is a named or keyed set of
>> peer attributes in a Dominion
>> Instance key: the unique name or set of attribute;value pairs that
>> uniquely identify this instance.
>> Domain: for each attribute there can exist a set of values called the
>> “domain” of that attribute.
>> Provenance:
>> https://www.w3.org/TR/prov-aq/
>> Provenance records for dynamic and context-dependent resources are
>> possible through a notion of constrained resources. A constrained
>> resource <https://www.w3.org/TR/prov-aq/#dfn-constrained-resource> is
>> simply a resource (in the sense defined by [WEBARCH
>> <https://www.w3.org/TR/prov-aq/#bib-WEBARCH>], section 2.2
>> <http://www.w3.org/TR/webarch/#id-resources>) that is a specialization
>> or instance of some other resource. For example, a W3C specification
>> typically undergoes several public revisions before it is finalized. A URI
>> that refers to the "current" revision might be thought of as denoting the
>> specification throughout its lifetime. Each individual revision would also
>> have its own target-URI <https://www.w3.org/TR/prov-aq/#dfn-target-uri>
>> denoting the specification at that particular stage in its development.
>> Using these, we can make provenance assertions that a particular revision
>> was published on a particular date, and was last modified by a particular
>> editor
>> Paul
>> Thoughts? , comments?
>> Thanks
>> PJAlagna@Gmail.com <PJAlagna@gmail.com>
>> 732-322-5641
Received on Monday, 25 May 2020 23:27:29 UTC

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