The scalability of otherwise of graph knowledge stores depends significantly on the hardware in question...
If one uses hardware with massive hardware multithreading, e.g. the Yarcdata http://www.yarcdata.com one will find massive graph databases quite efficient to deal with.... Currently commodity hardware just happens not to be very efficient for handling the common queries and manipulations one wants to do on large graphs... I tend to agree that graphs and hypergraphs are a natural representation for AGI. And I project that commodity hardware with high degrees of hardware multithreading is coming before too too long .. stuff like the Xeon Phi is a small step in that direction... -- Ben G On Wed, Jan 8, 2014 at 11:33 AM, Samantha Atkins <sjatk...@gmail.com> wrote: > Best representation of some information is a function of the operational > requirements on that information and subject to change over time. Perhaps > the best that can be achieved is efficient means to transform information > structures to be optimal for primary needs of the moment on the fly. It is > a trade-off of efficiency gained - cost of transformation. Even given a > particular representation there are interesting partitioning and grouping > questions relevant to real world efficiency. With sufficient metadata and > collected usage information it is conceivable information models can > transform to optimize themselves on the fly. Projects I have known of in > the past to do this did not seem highly successful. The exception is > caching in faster access media results previously computed. > > Graphs are not so scalable. At least this is the word from the graph > database community. > > > On Mon, Dec 16, 2013 at 3:37 PM, Aaron Hosford <hosfor...@gmail.com>wrote: > >> It can be represented as such. But propositions are not generally >> restricted to pairs of items. If you have a more complex logical structure, >> you would need more edges, whereas a single, more complex proposition would >> likely be used.* Mathematically, the two representational methods are >> identical in expressive power, but they lend themselves to different sorts >> of operations and have different representational advantages. A list can be >> used to represent a set, and sets can be used to represent a list, but if >> you need to represent lists, you probably don't want to go with sets, since >> you'll end up with something really awkward. Likewise, weighted knowledge >> graphs are naturally better suited to the representation of ambiguous >> information than logical propositions. >> >> *(Or do I have my terminology skewed? I used to call vertices nodes and >> edges links, to the confusion of anyone familiar with graph theory, since I >> don't get many opportunities to be corrected on my choice of words with >> regards to this stuff. By proposition, I mean a logical expression made up >> of Boolean operators applied to symbols, and possibly including references >> to truth functions.) >> >> >> On Mon, Dec 16, 2013 at 5:24 PM, Piaget Modeler < >> piagetmode...@hotmail.com> wrote: >> >>> One question, isn't an edge the same thing as a proposition? >>> >>> ~PM >>> >>> ------------------------------ >>> >>> Representing this in a propositional form (which is the interpretation I >>> give to the phrase "symbolic representation" -- correct me if I am wrong) >>> would require two separate propositions, each with a graded truth value, >>> but with no clear connection to each other. To record the relationship >>> between them -- that they are both candidate bindings for the anaphoric >>> noun phrase -- a third proposition would have to be created which contained >>> the first two as sub-propositions explicitly related to the noun phrase and >>> their respective weights. But with the weights contained inside the >>> proposition, each time a change was made to those weights, we would have a >>> new proposition, and would have to throw away the old one for being out of >>> date. (The other option being to make propositions mutable, which would >>> make search a nightmare.) Additionally, the number of ways to represent the >>> same information would grow combinatorically with each additional anaphoric >>> binding option, since the relationships are commutative. Trying to connect >>> this information together with other anaphoric ambiguities in the same >>> sentence would add another layer of combinatorics on top of the one we >>> already have. The proposition used to represent the full, ambiguous meaning >>> of a single sentence would be monstrous. >>> >>> Instead, with a graph, I can represent each option with a single >>> weighted edge, and there is precisely one, maximally compact representation >>> no matter how many options we have for any number of anaphoric noun >>> phrases. Making a change requires only a modification of a single weight, >>> or the addition of a single new edge, operating in-place without >>> significant effect on other nearby information structures. As another >>> advantage, when using graph form, we can take advantage of the many >>> algorithms from graph theory, and the explicit locality of reference for >>> related information, greatly speeding up and simplifying searches for >>> relevant information. I can have a vertex to represent alligators, and all >>> the information I know about alligators connected directly to it, meaning >>> that the system only needs to search vertices connected to the alligator >>> vertex for relevant information, rather than all information in the entire >>> database. I can use spreading activation to quickly find all the vertices >>> that relate both to alligators and to steeplechases, making it easy to >>> determine whether alligators can jump or run and thereby participate in >>> such a race. (A physical or other special-purpose model of alligator >>> behavior could have a reference to it stored under a particular vertex, >>> making that accessible just as quickly as any other data about them.) And >>> if I learn something new about alligators, I can add it without interfering >>> with the other information already present and immediately know which >>> information needs to be collated with the new data. Finally, it should be >>> clear that since graphs can be used to represent computer programs >>> (flowcharts) and data structures (pointer indirection networks), graphs are >>> representationally complete -- they can represent any thing or process that >>> can be represented on a computer in any way. So if our understanding can be >>> modeled computationally, as I believe it can, then graphs are up to the job. >>> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/23050605-2da819ff> | >>> Modify <https://www.listbox.com/member/?&;> Your Subscription >>> <http://www.listbox.com> >>> >> >> *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/2997756-fc0b9b09> | >> Modify <https://www.listbox.com/member/?&;> Your Subscription >> <http://www.listbox.com> >> > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/212726-deec6279> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Ben Goertzel, PhD http://goertzel.org "In an insane world, the sane man must appear to be insane". -- Capt. James T. Kirk ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
