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

Pat,

for completness sake, where do you place description logic (DL) in this
context as a foundation for semantics on the web?



On Wed, Jun 26, 2019 at 4:29 PM Patrick J Hayes <phayes@ihmc.us> wrote:

> A quick remark:
>
> On Jun 26, 2019, at 8:03 AM, Dave Raggett <dsr@w3.org> wrote:
>
> I very much agree and have been arguing for a blend of symbolic and
> statistical techniques using insights from decades of work in Cognitive
> Psychology.  Rational belief is about what can be justified given prior
> knowledge and past experience.
>
>
> So far in this thread we have been talking about knowledge representation
> notations. You are here talking about mechanisms, not quite the same topic.
> I entirely agree about the need to put together symbolic and statistical,
> but I don’t see any reason why the use of the statistical would change the
> nature or the semantics of the symbolic. (Do you?)
>
> This is not infallible, but nonetheless very useful in practice. It can
> support higher order reasoning, something that is essential for modelling
> human reasoning.
>
>
> What kind of higher-order reasoning are you referring to here? The term
> ‘higher-order’ has various meanings. If you simply mean that the logic can
> mention, describe and quantify over properties and relationships as
> first-class entities, then I would agree; but versions of FOL, even RDF,
> can do that.
>
> Here is a test case for what I called ‘classical higher-order’ in an
> earlier message. Do these facts:
>
> (P a)
> (Q b)
>
> entail this higher-order statement:
>
> exists (X) (X a) & (X b)
>
> ? If not, then your logic is not what I would call higher-order.
> The higher-order derivation mentions the property   lambda (x)( (P x) or
> (Q x) )
>
> Pat
>
>
> On 26 Jun 2019, at 14:54, Chris Harding <chris@lacibus.net> wrote:
>
> Formal logic is just one aspect of human reasoning (applied more or less
> correctly, depending on the human in question). Human reasoning has other
> aspects, giving it capabilities that formal logic does not have. For
> example, it can handle inconsistencies. If the goal of AI is to approximate
> human reasoning using computers, then its representational structures must
> go beyond those of formal logic.
>
> Patrick J Hayes wrote:
>
>
>
> On Jun 25, 2019, at 6:06 PM, Amirouche Boubekki <
> amirouche.boubekki@gmail.com> wrote:
>
>
>
> Le mar. 25 juin 2019 à 19:23, Patrick J Hayes <phayes@ihmc.us> a écrit :
>
>>
>>
>> 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?
>
>
> OK, this will take a little exposition. Notice up front that I said the
> results /suggest/ something, not that they establish it beyond all doubt.
>
> The main result in question is called Lindstrom’s theorem. What it says,
> technically, is that any logic (= a descriptive KR notation with a clear
> semantics) which satisfies two conditions must be no stronger than FOL. The
> two conditions are (1) compactness and (2) downward Lowenheim-Skolem (L-S).
> OK, I won’t try to prove this here, but it is a theorem, OK? So bear with
> me while I try to give an intuitive account of what these two conditions
> mean, and why they are plausibly required for computational effectiveness..
> They can be intuitively summarized as the conditions that proofs can be
> finitely wide and finitely deep.
>
> Compactness means that if something follows from a set of sentences, then
> it must follow from a finite subset of them. Put simply, proofs have to be
> finitely “wide”. This might seem kind of obvious, but there are quite
> natural logics which don’t satisfy it. For example, suppose we had some
> axioms for arithmetic which enabled one to prove that 0<1 and 1<2 and 2<3
> and… so on for every numeral N. Can you infer that x<x+1 for every number
> x? Seems obvious, but an actual proof of this would have infinitely many
> inputs. Compactness rules out things like this. Computationally this seems
> extremely plausible, since we cannot get an infinite proof into any
> physical memory.
>
> The (downward) L-S theorem is a bit harder to grok. It says that if a set
> of sentence in the logic has any satisfying interpretation, then it has a
> countable one. So if you can show that there isn't a countable one, then
> you know there isn’t one at all. So what? Well, the key point here has to
> do with how inference machinery operates. All inference systems can be seen
> as ways of surveying all possible interpretations, looking for
> counterexamples. You know that B follows from A when you can show that
> there are no counterxamples, ie no interpretations which make A and (not B)
> true. If your survey of interpretations is systematic and thorough, then
> your logical inference machinery is correct. But any computational search
> process can only generate finite structures. Now, /countably/ infinite
> structures are fine, because counterexamples will be finite and hence will
> be found eventually (this is based on a classical result called Koenig’s
> lemma). So, in brief, the L-S theorem condition means that a finite search
> through possible countable interpretations (which is the best that can be
> done with finite machines) can be an effective complete search, In other
> words, proofs that are finitely deep are enough, if the logic satisfies
> this condition. So logics that don’t (such as classical /higher-order/
> predicate logic) are kind of ruled out as computationally plausible logics
> anyway.
>
> OK, this is a very abbreviated summary of the reasoning, but the main
> takeaway point is that these conditions, although maybe a bit
> abstruse-seeming, really are very plausible conditions for any reasonable
> KR notation which comes with reasoning machinery. And Lindstrom’s theorem
> is, well, a theorem.
>
> Hope this helps.
>
>
> > So FOL seems like a good ‘reference’ benchmark for KR
>
> What about things like Probabilist Logic Network (or Bayesian networks)?
>
>
> I do not know for sure, but I would guess that a result similar to
> Lindstrom’s would apply to logics with any kind of truthvalues, including
> probabilities. My own, much more subjective view, is that probabilities are
> simply the wrong model for KR. For just one observation, people are
> absurdly poor at making probability estimates. But I won't try to justify
> this view here :-)
>
>
> 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.
>
>
> I might agree with that conclusion. For AI purposes, RDF is absurdly weak
> and inexpressive. But AI is not what it is trying to do.
>
> Pat
>
>
> 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:
>>
>>
>>
>> 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
>>
>>
>>
>>
>>
>>
>>
>>
>
> --
> Regards
>
> Chris
> ++++
>
> Chief Executive, Lacibus <https://lacibus.net/> Ltd
> chris@lacibus.net
>
>
> Dave Raggett <dsr@w3.org> http://www.w3.org/People/Raggett
> W3C Data Activity Lead & W3C champion for the Web of things
>
>
>
>
>
>
>
> --


---
Marco Neumann
KONA