Linas,

I'm giving it a thought...

For words similar to other words, or words near other words, directed
cyclic graphs with a few thousands unique nodes (spanning infinitely
because they are cyclic) seem the most promising food for thought. If you
could make CogServer (or analogous app) to output the node-relations set, I
could try to show them in a browser. Probably, additional search-for-word
feature would appy along the already seen browsing by dragging nodes around
to navigate them.

For skip-gram disjunct queries, I'm not completely sure how their output
data is interrelated. But if you can make the queries output
parent-children data, the rest would be easy.

- ivan -

ned, 6. ožu 2022. u 20:30 Linas Vepstas <[email protected]> napisao je:

>
>
> On Sat, Mar 5, 2022 at 3:08 PM Ivan V. <[email protected]> wrote:
>
>>
>> The logical step would be to prepare a CogServer instance filled with
>> those millions of atoms, keep it always running, and then query only what
>> is of the current interest to forward it to a browser.
>>
>
> Yes, exactly.
>
>
>> Anyway, who would browse over millions of atoms all at once? One might
>> only be interested in some subset of it, and if that subset can be measured
>> in thousands of atoms,
>>
>
> Or even just hundreds.  Or dozens.
>
>
>> Do you have any basic glimpse of a kind of visualization you'd like to
>> have? And what user interactions would pair it to be successful?
>>
>
> That's the hard question. It's hard to find good answers. I need your help
> finding good answers. Here are some ideas.  For example, given one word,
> find all the other words "related" to it. Order the list by the
> strength-of-relationship (and maybe show only the top-20). There are
> various different ways of defining "relatedness". One is to ask for all
> words that occur nearby, in "typical" text.  So, for example, if you ask
> about "bicycle", you might get back "bicycle wheel", "bicycle seat", "ride
> bicycle", "own bicycle".  Another might be to ask for "similar" words, you
> might get back "car", "horse", "bus", "motorcycle".   A third query would
> return skip-gram-like "disjuncts", of the form "ride * bicycle to *" or "*
> was on * bicycle" or "* travelled by bicycle * on foot"  -- stuff like
> that.  These are all fairly low-level relationships between words, and are
> the kind of datasets I have right now, today.
>
> My long-term goal, vision is to create a complex sophisticated network of
> information. Given that network, how can it be visualized, how can it be
> queried?  A classic answer would be a school-child homework assignment:
> "write 5 sentences about bicycles".   This would be a random walk through
> the knowledge network, converting that random walk into grammatically
> correct sentences (we're talking about how to do this in link-grammar, in a
> different email thread. It's hard.)
>
> Is there a way of visualizing this kind of random walk? Showing the local
> graph of things related to bicycles?
>
> So the meta-problem is: given a network of knowledge, how does one
> interact with it? How does one visualize it? How does one make it do
> things? If I pluck the word "bicycle" like a guitar string, how can I hear,
> see the vibrations of the network?
>
> -- Linas
>
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