Knowledge Graph Embedding Models
In PyKEEN, the base class for Knowledge Graph Embedding Models is
It combines entity and relation representations with an interaction function. On a very-high level, triple scores are obtained by first extracting the representations corresponding to the head and tail entity and relation (given as integer indices), and then uses the interaction interaction function to calculate a scalar score from them.
This tutorial gives a high-level overview of these components, and explains how to extend and modify them.
pykeen.nn.representation.Representation module provides a method to obtain representations, e.g.,
vectors, for given integer indices. These indices may correspond to entity or relation indices.
The representations are chosen by providing appropriate inputs to the parameters
entity_representations / entity_representations_kwargs for entity representations, or
relation_representations / relation_representations_kwargs for relation representations.
These inputs are then used to instantiate the representations using
pykeen.nn.representation_resolver.make_many(). Notice that the model class,
pykeen.models.ERModel, takes care of filling in the max_id parameter into the …_kwargs.
The default is to use a single
pykeen.nn.Embedding for entities and relations, as
encountered in many publications.
The following examples are for entity representations, but can be equivalently used for relation representations.
pykeen.nn.Embeddingwith dimensionality 64, suitable, e.g., for interactions such as
model = ERModel( # the default: # entity_representations=None, # equivalent to # entity_representations=[None], # equivalent to # entity_representations=[pykeen.nn.Embedding], entity_representations_kwargs=dict(shape=64), ..., )
pykeen.nn.Embeddingwith same dimensionality 64, suitable, e.g., for interactions such as
model = ERModel( entity_representations=[None, None], # note: ClassResolver.make_many supports "broad-casting" kwargs entity_representations_kwargs=dict(shape=64), # equivalent: # entity_representations_kwargs=[dict(shape=64), dict(shape=64)], ..., )
If you are unsure about which choices you have for chosing entity representations, take a look at the subclasses of
class_resolver library is used to support various alternative parametrization, e.g.,
the string name of a representation class, the class object, or instances of the
pykeen.nn.Representation class. You can also register your own classes to the resolver. Detailed
information can be found in the documentation of the package or Using Resolvers
An interaction function calculates scalar scores from head, relation and tail representations. These scores can be interpreted as the plausibility of a triple, i.e., the higher the score, the more plausible the triple is. Good models thus should output high scores for true triples, and low scores for false triples.
In PyKEEN, interactions are provided as subclasses of
pykeen.nn.Interaction, which is a
torch.nn.Module, i.e., it can hold additional (trainable) parameters, and can also be used outside of PyKEEN.
Its core method is
pykeen.nn.Interaction.forward(), which receives batches of head, relation and tail
representations and calculates the corresponding triple scores.
As with the representations, interactions passed to
pykeen.models.ERModel are resolved, this time using
pykeen.nn.interaction_resolver.make(). Hence, we can provide, e.g., strings corresponding to the interaction
function instead of an instantiated class. Further information can be found at Using Resolvers.
Interaction functions can require different numbers or shapes of entity and relation representations.
A symbolic description of the expected number of representations and their shape can be accessed by