Tuple Store, Artificial Science, Cognitive Science and RDF (Re: What is a Knowledge Graph? CORRECTION)

Le mar. 25 juin 2019 à 19:23, Patrick J Hayes <phayes@ihmc.us> a écrit :

>
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> 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.
>


> "Computably effective KR"

That is one of the issue I try to address.

> KR notation will not be /more/ expressive than FO logic

Citation?

> So FOL seems like a good ‘reference’ benchmark for KR

What about things like Probabilist Logic Network (or Bayesian networks)?

By the way, OpenCog projects was very suspicious of my work when I cited
RDF. If you are interested I can create a document describing how their
database
called atom space works, so called, hypergraph database.

And the those people are not alone. Other people told me RDF is deadend in
terms of
of (modern) KR for AI.

But still, I am here :)



>
>
> 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.
>

For the record, I don't try to undo that. As a new actor, I am working
toward it. As any newbie, I may ask some questions badly, that could lead
you to think that I want a revolution.


> 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.
>

Good question.


> 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.
>

Pragmatic self: forget about that part from specification?


> 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.
>

I would not say: "tuple stores are just [property] graph". Because my
implementation is much different. But I agree tuple store are some kind of
graph.

For the record, the idea of the n-tuple store (or chunks store) came from
the need to version a quad store to factor some code.
Later I discovered it could me useful in other contexts: provenance,
quality, space, some kind of time.
Again, the nstore, is a performance trick. What you can do with a triple
store you can do with nstore,
performance will be different, nstore should be faster. I am by no means
trying to force the WG to adopt the proposal I made on github
<https://github.com/w3c/sparql-12/issues/98>,
I hope to learn something from the conversation, and I already did.



> 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:
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>
>
> 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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