Well, intersection is just a disambiguation page, but the categories for intersection (set
theory) are good starting points for queries. Maybe I want to look at all concepts that
are in set theory and calculus. Or maybe I want to see all mathematical concepts except
for those in set theory and then sort them by the date of the first publication that
described them. I'd argue that these are potentially common scenarios that we want to
make easier for everyone.
I agree that there is no single perfect/master/universal ontology. Sure, our minds are all
rooted around our perceptions. If I say "dad" it conjures different images in
each of our heads. But if I say "Tom Cruise" our mental images are much more
similar. So there are large portions of our internal ontologies and mental representations
that we share, which are generally what we put in an encyclopedia for our culture.
Cultural differences are certainly fascinating and often follow linguistic barriers. The
Pirahã people don't have numbers, just one, two, and many, and their language can be
whistled. In standard psychology, a typical hurdle for self-awareness in children and
animals is the ability to find a spot painted on one's head by using a mirror. To try
and imagine an extreme, if I was the first bear to use Wikipedia I might want to make
things that can and cannot be eaten as the fundamental categories. Who knows? You can
certainly view the current category system as a graph with only one type of edge (a is a
member of b, or equivalently, b contains a). Having loops just means, for example, that
the graph can't be a tree, which isn't inherently bad. It just means that you have
to alter the definition of some concepts, like root, to fit a broader variety of possible
structures.
From: paul(a)ontology2.com
To: wikidata-l(a)lists.wikimedia.org
Date: Tue, 7 May 2013 13:50:10 +0000
Subject: Re: [Wikidata-l] Question about wikipedia categories.
Statistical methods can deal with black swans, but you've got to get
away from normal distributions and also model the risk that your model is
wrong.
Since training sets come from the same place sausage comes from,
training sets in machine learning rarely teach the algorithm the correct
prior distribution of the class. Punch a new prior into the system and it
will perform much better.
Some kinds of sampling biases can be somewhat overcome. Involvement of
multiple people smoothes out individual bias. (Kurzweil's project of
stealing a human soul with a neural network is already being scoops by
projects that are stealing statistical models of many souls.)
Language zone Wikipedias are obviously biased towards the viewpoint of
people in that language zone. Mostly that's a good thing, because a
Chinese knowledge base that reflected an Anglophone bias would seem
unnatural to Chinese speakers.
And that's the point. Useful systems don't "eliminate bias" but
they
are given the bias that they need in order to do their job.
I agree categories are most useful when they are the categories you
need. The toolbox above can help you estimate these with precision so high
that it's difficult to measure.
Arnold S isn't the best case for categories because humans,
bodybuilders, places, chemicals and such are well ontologized. Look at
the collection that comes up for the word "Intersection",
http://en.wikipedia.org/wiki/Intersection
Most of these are connected to the larger mass through just a few
categories that would be hard to express as restriction types. Wikipedia
is reasonable to require concepts to have a category because really, if you
want to assert something exists and can't find some category that this thing
is a member of, I wouldn't be so sure that this thing exists.
I'm not sure if there is anything I can't do with the current situation,
but bear in mind that I'm going to look at DBpedia, Wikidata and Freebase
facts too and be willing to do data cleaning processing and hand cleaning of
results that I cannot accept. It's a tricky and somewhat expensive process
(though it's cheaper than conventional ontology construction), so cleaner
data makes this process cheaper and quicker and available to more end users
personalized to their own needs to define the categories they need.
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