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 <gross...@gmail.com> 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 <alexand...@gmail.com> 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 opencog+u...@googlegroups.com. >>> To post to this group, send email to ope...@googlegroups.com. >>> 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 opencog+unsubscr...@googlegroups.com. > To post to this group, send email to opencog@googlegroups.com. > 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 opencog+unsubscr...@googlegroups.com. To post to this group, send email to opencog@googlegroups.com. 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/CACYTDBcLYGqSezT01zon9%2BUHB0b80%2BYukpBR1DCbfs9SHN6U7Q%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
