import torch.nn as nn
import torch
from .model import Model
from IPython import embed
import pdb
[docs]class RugE(Model):
"""`Knowledge Graph Embedding with Iterative Guidance from Soft Rules`_ (RugE), which is a novel paradigm of KG embedding with iterative guidance from soft rules.
Attributes:
args: Model configuration parameters.
epsilon: Caculate embedding_range.
margin: Caculate embedding_range and loss.
embedding_range: Uniform distribution range.
ent_emb: Entity embedding, shape:[num_ent, emb_dim].
rel_emb: Relation_embedding, shape:[num_rel, emb_dim].
.. _Knowledge Graph Embedding with Iterative Guidance from Soft Rules: https://ojs.aaai.org/index.php/AAAI/article/view/11918
"""
def __init__(self, args):
super(RugE, self).__init__(args)
self.args = args
self.ent_emb = None
self.rel_emb = None
self.init_emb()
[docs] def init_emb(self):
"""Initialize the entity and relation embeddings in the form of a uniform distribution.
"""
self.epsilon = 2.0
self.margin = nn.Parameter(
torch.Tensor([self.args.margin]),
requires_grad=False
)
self.embedding_range = nn.Parameter(
torch.Tensor([(self.margin.item() + self.epsilon) / self.args.emb_dim]),
requires_grad=False
)
self.ent_emb = nn.Embedding(self.args.num_ent, self.args.emb_dim * 2)
self.rel_emb = nn.Embedding(self.args.num_rel, self.args.emb_dim * 2)
nn.init.uniform_(tensor=self.ent_emb.weight.data, a=-self.embedding_range.item(), b=self.embedding_range.item())
nn.init.uniform_(tensor=self.rel_emb.weight.data, a=-self.embedding_range.item(), b=self.embedding_range.item())
[docs] def score_func(self, head_emb, relation_emb, tail_emb, mode):
"""Calculating the score of triples.
The formula for calculating the score is :math:`Re(< wr, es, e¯o >)`
Args:
head_emb: The head entity embedding.
relation_emb: The relation embedding.
tail_emb: The tail entity embedding.
mode: Choose head-predict or tail-predict.
Returns:
score: The score of triples.
"""
re_head, im_head = torch.chunk(head_emb, 2, dim=-1)
re_relation, im_relation = torch.chunk(relation_emb, 2, dim=-1)
re_tail, im_tail = torch.chunk(tail_emb, 2, dim=-1)
return torch.sum(
re_head * re_tail * re_relation
+ im_head * im_tail * re_relation
+ re_head * im_tail * im_relation
- im_head * re_tail * im_relation,
-1
)
[docs] def forward(self, triples, negs=None, mode='single'):
"""The functions used in the training phase
Args:
triples: The triples ids, as (h, r, t), shape:[batch_size, 3].
negs: Negative samples, defaults to None.
mode: Choose head-predict or tail-predict, Defaults to 'single'.
Returns:
score: The score of triples.
"""
head_emb, relation_emb, tail_emb = self.tri2emb(triples, negs, mode)
score = self.score_func(head_emb, relation_emb, tail_emb, mode)
return score
[docs] def get_score(self, batch, mode):
"""The functions used in the testing phase
Args:
batch: A batch of data.
mode: Choose head-predict or tail-predict.
Returns:
score: The score of triples.
"""
triples = batch["positive_sample"]
head_emb, relation_emb, tail_emb = self.tri2emb(triples, mode=mode)
score = self.score_func(head_emb, relation_emb, tail_emb, mode)
return score