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

Tensorlab? http://tensorlab.net/

On Thu, May 18, 2017 at 12:28 PM, 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



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Received on Friday, 19 May 2017 09:59:22 UTC