Knowledge Graph Embedding Models

In PyKEEN, the base class for Knowledge Graph Embedding Models is ERModel.

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 function to calculate a scalar score from them.

This tutorial gives a high-level overview of these components, and explains how to select them. A detailed discussions of possible representations can be found here: Representations. Interaction functions are discussed in detail in Interaction Functions.

Representation

A Representation 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 ERModel, takes care of filling in the max_id parameter into the ..._kwargs. The default is to use a single 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.

a single Embedding with dimensionality 64, suitable, e.g., for interactions such as TransEInteraction, or DistMultInteraction.

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),
    ...,
)

two Embedding with same dimensionality 64, suitable, e.g., for interactions such as BoxEInteraction

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)],
    ...,
)

Note

Internally, the 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 Representation class. You can also register your own classes to the resolver. Detailed information can be found in the documentation of the package or in the Using Resolvers tutorial

Interaction Function

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 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 forward(), which receives batches of head, relation, and tail representations and calculates the corresponding triple scores.

As with the representations, interactions passed to 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.

Note

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 pykeen.nn.Interaction.entity_shape and pykeen.nn.Interaction.relation_shape.