Source code for cogdl.models.emb.rotate

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