import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from .. import BaseModel, register_model
from .knowledge_base import KGEModel
[docs]@register_model("rotate")
class RotatE(KGEModel):
r"""
Implementation of RotatE model from the paper `"RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space"
<https://openreview.net/forum?id=HkgEQnRqYQ>`.
borrowed from `KnowledgeGraphEmbedding<https://github.com/DeepGraphLearning/KnowledgeGraphEmbedding>`
"""
def __init__(
self, nentity, nrelation, hidden_dim, gamma, double_entity_embedding=False, double_relation_embedding=False
):
super(RotatE, self).__init__(nentity, nrelation, hidden_dim, gamma, True, double_relation_embedding)
[docs] def score(self, head, relation, tail, mode):
pi = 3.14159265358979323846
re_head, im_head = torch.chunk(head, 2, dim=2)
re_tail, im_tail = torch.chunk(tail, 2, dim=2)
# Make phases of relations uniformly distributed in [-pi, pi]
phase_relation = relation / (self.embedding_range.item() / pi)
re_relation = torch.cos(phase_relation)
im_relation = torch.sin(phase_relation)
if mode == "head-batch":
re_score = re_relation * re_tail + im_relation * im_tail
im_score = re_relation * im_tail - im_relation * re_tail
re_score = re_score - re_head
im_score = im_score - im_head
else:
re_score = re_head * re_relation - im_head * im_relation
im_score = re_head * im_relation + im_head * re_relation
re_score = re_score - re_tail
im_score = im_score - im_tail
score = torch.stack([re_score, im_score], dim=0)
score = score.norm(dim=0)
score = self.gamma.item() - score.sum(dim=2)
return score