I have done some work on converting Wikidata items and properties to a
low-dimensional representation (graph embedding).
A webservice with a "most-similar" functionality based on computation in
the low-dimensional space is running from
https://tools.wmflabs.org/wembedder/most-similar/
A query may look like:
https://tools.wmflabs.org/wembedder/most-similar/Q20#language=en
It is based on a simple Gensim model
https://github.com/fnielsen/wembedder and could probably be improved.
It is described in
http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/7011/pdf/imm7011.pdf
It is not embedding statements but rather individual items.
There is general research on graph embedding. I have added some of the
scientific articles to Wikidata. You can see them with Scholia:
https://tools.wmflabs.org/scholia/topic/Q32081746
best regards
Finn Årup Nielsen
http://people.compute.dtu.dk/faan/
On 09/27/2017 02:14 PM, John Erling Blad wrote:
The most important thing for my problem would be to encode quantity and
geopos. The test case is lake sizes to encode proper localized descriptions.
Unless someone already have a working solution I would encode this as
sparse logarithmic vectors, probably also with log of pairwise differences.
Encoding of qualifiers is interesting, but would require encoding of a
topic map, and that adds an additional layer of complexity.
How to encode the values are not so much the problem, but avoiding
reimplementing this yet another time… ;)
On Wed, Sep 27, 2017 at 1:23 PM, Thomas Pellissier Tanon
<[email protected] <mailto:[email protected]>> wrote:
Just an idea of a very sparse but hopefully not so bad encoding (I
have not actually tested it).
NB: I am going to use a lot the terms defined in the glossary [1].
A value could be encoded by a vector:
- for entity ids it is a vector V that have the dimension of the
number of existing entities such that V[q] = 1 if, and only if, it
is the entity q and V[q] = 0 if not.
- for time : a vector with year, month, day, hours, minutes,
seconds, is_precision_year, is_precision_month, ..., is_gregorian,
is_julian (or something similar)
- for geo coordinates latitude, longitude, is_earth, is_moon...
- string/language strings: an encoding depending on your use case
...
Example : To encode "Q2" you would have the vector {0,1,0....}
To encode the year 2000 you would have {2000,0...,
is_precision_decade =
0,is_precision_year=1,is_precision_month=0,...,is_gregorian=true,...}
To encode a snak you build a big vector by concatenating the vector
of the value if it is P1, if it is P2... (you use the property
datatype to pick a good vector shape) + you add two cells per
property to encode is_novalue, is_somevalue. To encode "P31: Q5" you
would have a vector V = {0,....,0,0,0,0,1,0,....} with 1 only for
V[P31_offset + Q5_offset]
To encode a claim you could concatenate the main snak vector + the
qualifiers vectors that is the merge of the snak vector for all
qualifiers (i.e. you build the vector for all snak and you sum them)
such that the qualifier vectors encode all qualifiers at the same
time. it allows to check that a qualifiers is set just by picking
the right cell in the vector. But it will do bad things if there are
two qualifiers with the same property and having a datatype like
time or geocoordinates. But I don't think it really a problem.
Example: to encode the claim with "P31: Q5" main snak and qualifiers
"P42: Q42, P42: Q44" we would have a vector V such that V[P31_offset
+ Q5_offset] = 1, V[qualifiers_offset + P42_offset + Q42_offset] = 1
and V[qualifiers_offset + P42_offset + Q44_offset] = 1 and 0 elsewhere.
I am not sure how to encode statements references (merge all of them
and encode it just like the qualifiers vector is maybe a first step
but is bad if we have multiple references). For the rank you just
need 3 booleans is_preferred, is_normal and is_deprecated.
Cheers,
Thomas
[1] https://www.wikidata.org/wiki/Wikidata:Glossary
<https://www.wikidata.org/wiki/Wikidata:Glossary>
> Le 27 sept. 2017 à 12:41, John Erling Blad <[email protected]
<mailto:[email protected]>> a écrit :
>
> Is there anyone that has done any work on how to encode statements as
features for neural nets? I'm mostly interested in sparse encoders for online
training of live networks.
>
>
> _______________________________________________
> Wikidata mailing list
> [email protected] <mailto:[email protected]>
> https://lists.wikimedia.org/mailman/listinfo/wikidata
<https://lists.wikimedia.org/mailman/listinfo/wikidata>
_______________________________________________
Wikidata mailing list
[email protected] <mailto:[email protected]>
https://lists.wikimedia.org/mailman/listinfo/wikidata
<https://lists.wikimedia.org/mailman/listinfo/wikidata>
_______________________________________________
Wikidata mailing list
[email protected]
https://lists.wikimedia.org/mailman/listinfo/wikidata
_______________________________________________
Wikidata mailing list
[email protected]
https://lists.wikimedia.org/mailman/listinfo/wikidata
