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Python library to compute knowledge graph embeddings
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README.md

Knowledge Graph Embeddings

scikit-kge is a Python library to compute embeddings of knowledge graphs. The library consists of different building blocks to train and develop models for knowledge graph embeddings.

To compute a knowledge graph embedding, first instantiate a model and then train it with desired training method. For instance, to train holographic embeddings of knowledge graphs (HolE) with a logistcc loss function:

from skge import HolE, StochasticTrainer

# Load knowledge graph 
# N = number of entities
# M = number of relations
# xs = list of (subject, object, predicte) triples
# ys = list of truth values for triples (1 = true, -1 = false)
N, M, xs, ys = load_data('path to data')

# instantiate HolE with an embedding space of size 100
model = HolE((N, N, M), 100)

# instantiate trainer
trainer = StochasticTrainer(model)

# fit model to knowledge graph
trainer.fit(xs, ys)

See the repository for the experiments in the HolE paper for an extensive example how to use this library.

The different available buildings blocks are described in more detail in the following:

Model

Instantiating a model, e.g. HolE

model = HolE(
    self.shape,
    self.args.ncomp,
    init=self.args.init,
    rparam=self.args.rparam
)

Trainer

scikit-kge supports two basic ways to train models:

StochasticTrainer (skge.base.StochasticTrainer)

Trains a model with logistic loss function

trainer = StochasticTrainer(
    model,
    nbatches=100,
    max_epochs=500,
    post_epoch=[self.callback],
    learning_rate=0.1
)
self.trainer.fit(xs, ys)
PairwiseStochasticTrainer (skge.base)

To train a model with pairwise ranking loss

trainer = PairwiseStochasticTrainer(
    model,
    nbatches=100,
    max_epochs=500,
    post_epoch=[self.callback],
    learning_rate=0.1,
    margin=0.2,
    af=af.Sigmoid
)
self.trainer.fit(xs, ys)

Parameter Update

scitkit-kge supports different methods to update the parameters of a model via the param_update keyword of StochasticTrainer and PairwiseStochasticTrainer.

For instance,

from skge.param import AdaGrad

trainer = StochasticTrainer(
    ...,
    param_update=AdaGrad,
    ...
)

uses AdaGrad to update the parameter.

Available parameter update methods are

SGD (skge.param.SGD)

Basic stochastic gradient descent. Only parameter is the learning rate.

AdaGrad (skge.param.AdaGrad)

AdaGrad method of Duchi et al., 2011. Automatically adapts learning rate based on gradient history. Only parameter is the initial learning rate.

Sampling

sckit-kge implements different strategies to sample negative examples.

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