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Re: What is a Knowledge Graph? CORRECTION

From: Paola Di Maio <paoladimaio10@gmail.com>
Date: Wed, 24 Jul 2019 14:38:43 +0800
Message-ID: <CAMXe=SrdWY7kSr_NWyX-JvyJrsVVbRmehUx89wEZyx=UGg+AAw@mail.gmail.com>
To: W3C AIKR CG <public-aikr@w3.org>
Cc: semantic-web <semantic-web@w3.org>
One further note on the Knowledge Graph discussion

The reliance on KG (both as a word/naming convention, as as a mechanism for
KR)
is a concern, and somewhat can be cause of awful distortions - which will
impact perception and decision making

The literature analysis by Mike Bergman is very valuable in that it helps
us to understand what happened, how KG evolved
http://www.mkbergman.com/2244/a-common-sense-view-of-knowledge-graphs/

Mike, can you please specify which knowledge base/endpoint and what software
was used to generate the Figures 1 and 2 - could not find that info in the
post
would like to replicate /tweak your citation diagrams

I find this thesis and some of the conclusions, important:
/pdfs.semanticscholar.org/d768/76495f20493243bb862fce467534226c8bd5.pdf?_ga=2.9759605.1126947389.1563945860-1966063881.1561180249


QUOTE

e believe that these developments will continue to shift the attention of
industry and researchfrom the document-centric information processing
towards a facts or KG-based perspective inwhich information will no longer
be a collection of natural language text (documents) – butinstead be
handled at the level of (single or connected) facts. In the long-term
perspective, how-ever, when natural language generation from such fact
knowledge bases will work sufficientlywell, the users will no longer
interact directly with neither documents nor facts, but only askquestions
that an advanced search-engine interface will answer, while the complexity
of KGgeneration, information processing, reasoning, and question answering
will be hidden.
UNQUOTE

Namely the limitations of KG
- cannot help to represent the truth/fact checking
-  human understanding and decision making will be based  on machine based
truth
assertions (representationS) that humans cannot easily check/understand
But fact checking cannot be fully automated!!

In relation to AI, and SW, separating the human representation from the
machine representation (KG)
may prevent not only fact checking but also error correction. arghhhh

I hope that future work in KG  tackles these gaps


PDM







On Tue, Jul 2, 2019 at 10:43 PM ProjectParadigm-ICT-Program <
metadataportals@yahoo.com> wrote:

> Mike and Hans,
>
> Knowledge is much more than extracting structure from facts and data. If I
> just recall that the collection of facts is subject to the uncertainty
> principle, any structure deduced cannot be complete, and the application of
> free will, and/or axiom of choice create a dichotomy, knowledge is much
> more.
>
> We are limited by our sensory apparatus, our hard wiring in our human
> brain, including the shortcuts made when processing visual data, and the
> limitations of natural language.
>
> I agree that knowledge reasoning should be fairly straightforward, but
> making the jump from KR to knowledge itself implies we come up with some
> consistent many worlds modeling scheme in which the virtual, mathematical
> and (many interpretations of) the physical world coexist, reconciling
> incompleteness, uncertainty principle, sensory limitations and application
> of free will and choice.
>
> A convergence of efforts by string theorists, researchers in human brain
> cognitive and biological structure fields, theoretical physicists and
> mathematicians working on finite groups, category theory, algebraic
> topology and logical structures for consistent super theories, and an odd
> mix of linguists and philosophers (including Buddhists) is doing just that.
>
> But they are far from a consensus.
>
> The point I am trying to make is that KR is more than semantics and
> ontologies and knowledge graphs, graphs, category theory diagrams and
> Feynmann diagrams and any other visualization tools we use.
>
> The implicate order David Bohm theoreticized underlying quantum reality
> and the reality of our physical world, cannot be captured by some mix of
> formal logic, semantic structures, ontologies or computable frameworks.
>
> And we we want someday A(G)I to be able to grasp human knowledge in
> general, we must create a growth path towards formal structures which have
> meta-layers above (knowledge) graphs, formal logic and ontologies.
>
> Mathematically speaking, using formal logic, ontologies and generalized
> graphs is necessary but insufficient for this general formal structure.
>
> 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 Tuesday, July 2, 2019, 7:38:19 AM ADT, hans.teijgeler@quicknet.nl <
> hans.teijgeler@quicknet.nl> wrote:
>
>
> Hi Mike,
>
>
>
> What we collect are facts, not knowledge. This fact collection is to be
> done during the entire existence of a process plant.
>
> Once you have enough facts you might start extracting knowledge from them,
> for example which kind of pump seal is best in a given service, or what to
> do to optimize energy usage.
>
> This derivation may take place by reasoning, statistical analysis, etc.
>
>
>
> ISO 15926 deals with facts by excluding modalities by adopting On the
> Plurality of Worlds
> <http://15926.org/topics/possible-worlds/Lewis-David-(1986)-On-the-Plurality-of-Worlds.pdf> of
> David K. Lewis <https://nl.wikipedia.org/wiki/David_Kellogg_Lewis> as
> summarized in http://15926.org/topics/possible-worlds/ .
>
> This allows for modeling designed objects in a separate possible world in
> the same manner as in the real world.
>
>
>
> Regards, Hans
>
> _____________________________________________
>
> *From:* Mike Bergman <mike@mkbergman.com>
> *Sent:* dinsdag 2 juli 2019 09:49
> *To:* hans.teijgeler@quicknet.nl; 'Patrick J Hayes' <phayes@ihmc.us>;
> 'ProjectParadigm-ICT-Program' <metadataportals@yahoo.com>
> *Cc:* 'Dave Raggett' <dsr@w3.org>; 'Paola Di Maio' <
> paoladimaio10@gmail.com>; 'Amirouche Boubekki' <
> amirouche.boubekki@gmail.com>; 'Chris Harding' <chris@lacibus.net>;
> 'xyzscy' <1047571207@qq.com>; 'semantic-web' <semantic-web@w3.org>
> *Subject:* Re: What is a Knowledge Graph? CORRECTION
>
>
>
> Hi All,
>
> My take on the question:
>
> http://www.mkbergman.com/2244/a-common-sense-view-of-knowledge-graphs/
>
> Mike
>
> On 6/25/2019 11:40 PM, hans.teijgeler@quicknet.nl wrote:
>
> Hi Pat,
>
>
>
> +1 , that’s why we (the process industries) have an upper ontology,
> defined in ISO 15926-2 <http://15926.org/topics/data-model/index.htm>,
> with 218 entity types and the reference data library of ISO 15926-4
> <http://15926.org/topics/reference-data/index.htm> with 39,000 classes.
>
> Application data are mapped to templates (212 small models, each using
> some of those 218 entity types), in RDF, validated with SHACL, and stored
> in a triple store.
>
> Although this doesn’t cover the entire universe, it does cover the
> technical and activity life-cycle information of a process plant (oil,
> chemical, food, etc), integrated from cradle to grave.
>
>
>
> Regards, Hans
>
> __________________________________________
>
> *From:* Patrick J Hayes <phayes@ihmc.us> <phayes@ihmc.us>
> *Sent:* dinsdag 25 juni 2019 19:22
> *To:* ProjectParadigm-ICT-Program <metadataportals@yahoo.com>
> <metadataportals@yahoo.com>
> *Cc:* Dave Raggett <dsr@w3.org> <dsr@w3.org>; Paola Di Maio
> <paoladimaio10@gmail.com> <paoladimaio10@gmail.com>; Amirouche Boubekki
> <amirouche.boubekki@gmail.com> <amirouche.boubekki@gmail.com>; Chris
> Harding <chris@lacibus.net> <chris@lacibus.net>; xyzscy
> <1047571207@qq.com> <1047571207@qq.com>; semantic-web
> <semantic-web@w3.org> <semantic-web@w3.org>
> *Subject:* Re: What is a Knowledge Graph? CORRECTION
>
>
>
>
>
>
>
>
> On Jun 23, 2019, at 5:35 PM, ProjectParadigm-ICT-Program <
> metadataportals@yahoo.com> wrote:
>
>
>
> Again, let us look at the issue at hand. Artificial intelligence requires
> we represent knowledge in some format. All forms brought to the fore so far
> stick to a pretty simple way of representing knowledge.
>
>
>
> Most (all?) of the KR proposals put forward in AI or cognitive science
> work have been some subset of first-order predicate logic, using a variety
> of surface notations. There are some fairly deep results which suggest that
> any computably effective KR notation will not be /more/ expressive than FO
> logic. So FOL seems like a good ‘reference’ benchmark for KR expressivity.
>
>
>
>
>
> What we should be looking for is a generalized form in which objects can
> be linked. The graph is an obvious form.
>
> But we are focusing to much on the nuts and bolts level.
>
>
>
> Since it is the generally accepted intention to use AI in all walks of
> professional, commercial, personal and academic life, we should be looking
> at the various ways of representing knowledge.
>
>
>
> Otherwise we end up creating knowledge representation silos.
>
>
>
> Avoiding KR silos was one of the primary goals of the entire semantic-web
> linked-data initiative. But this has many aspects. First, we need to agree
> to all use a common basic notation. Triples (=RDF =Knowledge Graph
> =JSON-LD) has emerged as the popular choice. Getting just this much
> agreement has taken 15 years and thousands of man-hours of strenuous effort
> and bitterly contested compromises, so let us not try to undo any of that,
> no matter what the imperfections are of the final choice.
>
>
>
> The next stage, which we are just getting started on, involves agreeing on
> a common vocabulary for referring to things, or perhaps a universal
> mechanism for clearly indicating that your name for something means the
> same as my name for that same thing. This seems to be much harder than the
> semantic KR pioneers anticipated.
>
>
>
> The third stage involves having a global agreement on the ontological
> foundations of our descriptions, what used to be called the ‘upper level
> ontology’. This is where we get into actual metaphysical disagreements
> about the nature of reality (are physical objects extended in time? How do
> we handle vague boundaries? What are the relationships between written
> tokens, images, symbols, conventions and the things they represent? What is
> a ‘background’? What is a ‘shape’? Is a bronze statue the same kind of
> thing as a piece of bronze? What changes when someone signs a contract?
> Etc. etc., etc.) This is where AI-KR and more recently, applied ontology
> engineering (not to mention philosophy) has been working for the past 40 or
> 50 years, and I see very little hope of any clear agreements acceptable to
> a large percentage of the world’s users.
>
>
>
>
>
>
> Category theory diagrams, graphs and Feynman diagrams are three well known
> forms of representing knowledge graphs, but only in semantic web
> technologies we specify tuples, a restrictive form of representation.
>
>
>
> Category diagrams and Feynman diagrams are meaningful only within highly
> restricted and formal fields (category theory and quantum physics,
> respectively) so have little to do with general KR. If your point is that
> diagrams are useful, one can of course point to many examples of them being
> useful to human users, but this does not make them obviously useful in
> computer applications.
>
>
>
> Tuples are not more restrictive than graphs, since a collection of tuples
> is simply one way to implement a graph. Tuple stores ARE graphs.
>
>
>
> Best wishes
>
>
>
> Pat Hayes
>
>
>
>
>
>
> 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 Sunday, June 23, 2019, 3:57:01 AM ADT, Paola Di Maio <
> paoladimaio10@gmail.com> wrote:
>
>
>
>
>
>
>
>
>
> Chunks are also used in NLP (which is part of/related to CS either way)
>
> aka tokens
>
> Various useful references come up on searching chunks as tokens
>
>
>
> https://docs.oasis-open.org/dita/v1.2/os/spec/archSpec/chunking.html
>
>
> https://www.oxygenxml.com/doc/versions/21.1/ug-editor/topics/eppo-chunking.html
>
>
>
> On Sun, Jun 23, 2019 at 1:12 AM Dave Raggett <dsr@w3.org> wrote:
>
>
>
>
>
>
> On 22 Jun 2019, at 14:54, Amirouche Boubekki <amirouche.boubekki@gmail.com>
> wrote:
>
>
>
> Le ven. 21 juin 2019 à 16:27, Dave Raggett <dsr@w3.org> a écrit :
>
> Researchers in Cognitive Science have used graphs of chunks to represent
> declarative knowledge for decades, and chunk is their name for an n-tuple.
>
>
>
> I tried to lookup "graph of chunks" related to cognitive science. I could
> not find anything interesting outside this white paper about "accelerating
> science" [0] that intersect with my goals.
>
>
>
> [0]
> https://cra.org/ccc/wp-content/uploads/sites/2/2016/02/Accelerating-Science-Whitepaper-CCC-Final2.pdf
>
>
>
> Chunks are used on cognitive architectures, such as ACT-R, SOAR and
> CHREST, and is inspired by studies of human memory recall, starting with
> George Miller in 1956, and taken further by a succession of researchers.
> Gobet et al. define a chunk as “a collection of elements having strong
> associations with one another, but weak associations with elements within
> other chunks.” Cognitive Science uses computational models as the basis for
> making quantitive descriptions of different aspects of cognition including
> memory and reasoning. There are similarities to Frames and Property Graphs.
>
>
>
> Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
>
> W3C Data Activity Lead & W3C champion for the Web of things
>
>
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Received on Wednesday, 24 July 2019 06:39:47 UTC

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