Hi Linas, Thank you for your responses, and the pointer.
It seems to me that your example further pin-points my question: A quasi-linear walk through a semantic network is essentially a constructed structure (or path) through the use of grammar, to get at a possible reading of a sentence that would make sense to a person within a "semantic space", without however capturing meaning per-se. A lexicon, say, "merely' captures the rules of constructions of particular given verbs and nouns *based* on their human interpreted meaning). Hence, grammar's purpose seems to really "only" to construct a meanginful path rather than tell us what the meaning of the knowledge embodied in that path is. The latter seems to require another "kind" of semantics/meaning (and perhaps some might say that there are turtles all the way down -- or at least until some grounding). does my intuition make sense, thank you, Daniel On Thursday, 20 April 2017 16:59:38 UTC+3, linas wrote: > > Semantics and syntax are two different things. Syntax allows you to parse > sentences. Semantics is more about how concepts inter-relate to each other. > -- a network. A sentence tends to be a quasi-linearized walk through such > a network. For example, take a look at the "deep" and the "surface" > structures in meaning-text theory. From there, one asks "what kind of > speech acts are there?" and "why do people talk?" and this is would be the > "next level", beyond the homework exercise I mentioned in the previous > email. > > --linas > > On Wed, Apr 19, 2017 at 7:23 PM, Daniel Gross <[email protected] > <javascript:>> wrote: > >> Hi Ben, >> >> Thank you for your response. I started reading the paper and was >> wondering if you could help me clarify a confusion i apparently have when >> it comes to the meaning of meaning: >> >> How is linguistic meaning connected to human embodied meaning that we >> would call human (or AGI) understanding. >> >> Linguistic meaning seems to be about the linguistic meta-language that >> shows how a human would parse a sentence unambiguously, so that a human >> can, in principle, understand the meaning of a sentence, although, what is, >> say, instructed by a sentence, as understood by a human seems not captured, >> but would require more machinery. >> >> In this sense, linguistic machinery seems to embody (as a theory of mind) >> how humans understand (in a cognitive economical manner), rather than what >> humans understand --at least this is what confuses me ... >> >> any thought would be much appreciated ... >> >> thank you, >> >> Daniel >> >> >> >> >> >> On Wednesday, 19 April 2017 09:16:42 UTC+3, Ben Goertzel wrote: >>> >>> We have a probabilistic logic engine (PLN) which works on (optionally >>> probabilistically labeled) logic expressions.... This logic engine >>> can also help with extracting semantic information from natural >>> language or perceptual observations. However, it's best used together >>> with other methods that carry out "lower levels" of processing in >>> feedback and cooperation with it... >>> >>> In the case of vision, Ralf Mayet is leading an effort to use a >>> modified InfoGAN deep NN to extract semantic information from >>> images/videos/sounds to pass into PLN, the Pattern Miner, and so forth >>> >>> In the case of textual language, Linas is leading an effort to extract >>> a first pass of semantic and syntactic information from unannotated >>> text corpora via this general approach >>> >>> https://arxiv.org/abs/1401.3372 >>> >>> The same approach should work when non-textual groundings are included >>> in the corpus, or when the learning is real-time experiential rather >>> than batch-based.... but there's plenty of nitty-gritty work here... >>> >>> ben goertzel >>> >>> On Wed, Apr 19, 2017 at 7:23 AM, Daniel Gross <[email protected]> >>> wrote: >>> > Hi Linas, >>> > >>> > How do you propose to learn an ontology from the data -- also, what >>> purpose >>> > would, in your opinion, the learned ontology serve. Or stated >>> differently, >>> > in what way are you thinking to engender higher-level cognitive >>> capabilities >>> > via machine learned bundled neuron (and implicit ontologies, perhaps). >>> > >>> > thank you, >>> > >>> > Daniel >>> > >>> > >>> > On Wednesday, 19 April 2017 03:40:47 UTC+3, linas wrote: >>> >> >>> >> >>> >> >>> >> On Tue, Apr 18, 2017 at 3:22 PM, Alex <[email protected]> wrote: >>> >>> >>> >>> Maybe we can solve the problem about modelling classes (and using OO >>> and >>> >>> UML notions for knowledge representation) with the following >>> (pseudo)code >>> >>> >>> >>> - We can define ConceptNode "Object", that consists from the set or >>> >>> properties and functions >>> >>> >>> >>> - We can require that any class e.g. Invoice is the inherited from >>> the >>> >>> Object: >>> >>> IntensionalInheritanceLink >>> >>> Invoice >>> >>> Object >>> >>> >>> >>> - We can require that any more specifica class, e.g. VATInvoice is >>> the >>> >>> inherited from the more general class: >>> >>> IntensionalInheritanceLink >>> >>> VATInvoice >>> >>> Invoice >>> >>> >>> >>> - We can require that any instance is inherited from the concrete >>> class: >>> >>> ExtensionalInheritanceLinks >>> >>> invoice_no_2314 >>> >>> VATInvoice >>> >> >>> >> >>> >> If you wish, you can do stuff like that. opencog per se is agnostic >>> about >>> >> how you do this, you can do it however you want. The proper way to do >>> this >>> >> is discussed in many places; for example here: >>> >> https://en.wikipedia.org/wiki/Upper_ontology >>> >> >>> >> I'm not particularly excited about building ontologies by hand, its >>> much >>> >> more interesting (to me) to understand how they can be learned >>> >> automatically, from raw data. >>> >>> >>> >>> >>> >>> But I don't know yet what can and what can not be the parent for >>> >>> extensional and intensional inheritance. Can an entity be >>> extensionally >>> >>> inherited from the more complex object or it can be extensionally >>> inherited >>> >>> from empty set-placeholder only. When we introduce notion of set, >>> then the >>> >>> futher question always arise - does OpenCog make distinction between >>> sets >>> >>> and proper classes? >>> >> >>> >> >>> >> Why? This "distinction" only matters if you want to implement set >>> theory. >>> >> My pre-emptive strike to halt this train of thought is this: Why >>> would you >>> >> want to implement set theory, instead of, say, model theory or >>> universal >>> >> algebra, or category theory, or topos theory? why the heck would >>> >> distinguishing a set-theoretical-set from a >>> set-theoretical-proper-class >>> >> matter? (which oh by the way is similar but not the same thing as a >>> >> category-theoretic-proper-class...) >>> >> >>> >> You've got multiple ideas going here, at once: the best way to >>> hand-craft >>> >> some ontology; the best theoretical framework to do it in; the >>> philosophy of >>> >> knowledge representation in general... and, my personal favorite: how >>> do I >>> >> get the machine to do this automatically, without manual >>> intervention? >>> >> >>> >>> >>> >>> >>> >>> There is second problem as well - there is only one - mixed >>> >>> InheritanceLink. One can use SubsetLink for the extensional >>> inheritance >>> >>> (still it feels strange), but there is certainly necessary syntactic >>> sugar >>> >>> for intensional inheritance, because it is hard to write and read >>> SubsetLink >>> >>> of property sets again and again >>> >>> (http://wiki.opencog.org/w/InheritanceLink). >>> >> >>> >> >>> >> If the machine has learned an ontology with a million subset links in >>> it, >>> >> no human being is ever going to read or want to read that network. >>> It'll be >>> >> like looking at a bundle of neurons: the best you can do is say "oh >>> wow, a >>> >> bundle of neurons!" >>> >> >>> >> --linas >>> >>> >>> >>> >>> >>> -- >>> >>> You received this message because you are subscribed to the Google >>> Groups >>> >>> "opencog" group. >>> >>> To unsubscribe from this group and stop receiving emails from it, >>> send an >>> >>> email to [email protected]. >>> >>> To post to this group, send email to [email protected]. >>> >>> Visit this group at https://groups.google.com/group/opencog. >>> >>> To view this discussion on the web visit >>> >>> >>> https://groups.google.com/d/msgid/opencog/a6d0102e-9ca1-4204-8dd4-75a9fb2ec06b%40googlegroups.com. >>> >>> >>> >>> >>> >>> For more options, visit https://groups.google.com/d/optout. >>> >> >>> >> >>> > -- >>> > You received this message because you are subscribed to the Google >>> Groups >>> > "opencog" group. >>> > To unsubscribe from this group and stop receiving emails from it, send >>> an >>> > email to [email protected]. >>> > To post to this group, send email to [email protected]. >>> > Visit this group at https://groups.google.com/group/opencog. >>> > To view this discussion on the web visit >>> > >>> https://groups.google.com/d/msgid/opencog/01d0f8ad-2c6c-44af-9e46-fc71e2f2559f%40googlegroups.com. >>> >>> >>> > >>> > For more options, visit https://groups.google.com/d/optout. >>> >>> >>> >>> -- >>> Ben Goertzel, PhD >>> http://goertzel.org >>> >>> "I am God! I am nothing, I'm play, I am freedom, I am life. I am the >>> boundary, I am the peak." -- Alexander Scriabin >>> >> > -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/9ec7d280-dcc6-44f1-b5ae-8b9731d280a0%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
