Re: Looking for: tensor-library for sparse tensor based triple store

Please keep us posted on how this work progresses.

I'm particularly interested; we have recently begun work on a
framework (or at least a set of repeatable processes) for assembling
tensors based on experimental data persisted to triplestore-based
knowledge graphs.

Thanks!

John

On Mon, Jun 12, 2017 at 7:48 AM, Alexander Bigerl
<bigerl@informatik.uni-leipzig.de> wrote:
> Thank you both. Especially tensorlab looks promising.
>
> Best,
> Alex
>
>
> Am Freitag, den 19.05.2017, 05:58 -0400 schrieb John Erickson:
>
> Tensorlab? http://tensorlab.net/
>
>
>
> Am Freitag, den 19.05.2017, 16:10 +0200 schrieb Jörn Hees:
>
> RESCAL? https://github.com/mnick/rescal.py
>
> Best,
> Jörn
>
>
> On 18 May 2017, at 18:28, Alexander Bigerl
> <bigerl@informatik.uni-leipzig.de> wrote:
>
> Hi everyone,
>
> I am working on a tensor-based triple store to query triple patterns (not
> full SPARQL). Therefor I'm looking for a suitable library supporting sparse
> tensor product. The programming language doesn't matter. But it would be
> nice if it was optimized for orthonormal-based tensors (means it doesn't
> need to distinguish between co- and contravariant dimensions for
> multiplication).
>
> In more detail:
>
> I represent my my data like this:
>
>  • I have tensors storing boolean values.
>
>  • They are n >= 3 dimensional and every dimension has the same size
> m>1000000.
>
>  • Every dimension uses a natural number index 0...m.
>
>  • The tensors are orthonormal-based so I don't need to distinguish between
> co- and contraviarant dimensions.
>
>  • There are only very few true values in every tensor, so the rest of the
> values is false. Therefor it should be sparse. Non-sparse is no option
> because of at least 1000000^3 entries.
>
> I'm looking for:
>
>  • efficient sparse n-D tensor implementation with support of a fast inner
> product like: Tαγβ • Dβδε = Rαγδε
>
>  • optional: support for pipelining multiple operations
>
>  • optional: support for logical and or pointwise multiplication of
> equal-dimensioned tensors.
> The following libraries don't do the trick for reasons:
>
>  • Tensor flow: misses multiplication with non-dense-none-2D-matrices
>  • scipy sparse: supports only 2D representation and would output a dense
> narray for dotproduct
>  • theano: supports only 2D sparse tensors
>  • Shared Scientific Toolbox and Universal Java Matrix Package: don't
> support multiplication of n-D sparse tensors
> Who is wandering now where the triples are: They are mapped to the
> dimensions' index so that the coordinates of a true in a 3D Tensor
> represents a triple.
>
> I would be very thankful for any comments or recommendations.
>
> Kind regards,
>
> Alexander Bigerl
>
>
>
>
>



-- 
John S. Erickson, Ph.D.
Director of Operations, The Rensselaer IDEA
Deputy Director, Web Science Research Center (RPI)
<http://idea.rpi.edu/> <olyerickson@gmail.com>
Twitter & Skype: olyerickson

Received on Monday, 12 June 2017 12:09:23 UTC