umls_tnsr

The dataset is a list that contains UMLS dataset basic graph used by Nickel et al 2011 to 
 test <code><a rd-options="" href="/link/rescal?package=RDFTensor&version=1.3" data-mini-rdoc="RDFTensor::rescal">rescal</a></code> .

datasets

UMLS

Different Tensor Factorization techniques suitable for RDF Tensors.
RDF Tensors are three-mode-tensors, binary tensors and usually very sparse.
Currently implemented methods are
'RESCAL' Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel (2012) <doi:10.1145/2187836.2187874>,
'NMU' Daniel D. Lee and H. Sebastian Seung (1999) <doi:10.1038/44565>,
'ALS', Alternating Least Squares
'parCube' Papalexakis, Evangelos, C. Faloutsos, and N. Sidiropoulos (2012) <doi:10.1007/978-3-642-33460-3_39>,
'CP_APR' C. Chi and T. G. Kolda (2012) <doi:10.1137/110859063>.
The code is mostly converted from MATLAB and Python implementations of these methods.
The package also contains functions to get Boolean (Binary) transformation of the real-number-decompositions.
These methods also are for general tensors, so with few modifications they can be applied for other types of tensor.

Abdelmoneim Desouki

RDFTensor

Different Tensor Factorization (Decomposition) Techniques for
RDF Tensors (Three-Mode-Tensors)

umls_tnsr function

slot X contains the frontal slices and SO contains the names of the entities 
and P contains the names of the predicates

Format

The dataset is a list that contains UMLS dataset basic graph used by Nickel et al 2011 to 
 test <code><a rd-options='' href='rescal'>rescal</a></code> .

umls_tnsr: RDF tensor of UMLS small graph

Description

Usage

Arguments

Format

References