知识图谱ComplEX代码实现

根据提供的引用内容，可以了解到知识图谱嵌入方法中的一种常用方法是ComplEX。下面是一个简单的ComplEX代码实现的示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ComplEX(nn.Module):
    def __init__(self, num_entities, num_relations, embedding_dim):
        super(ComplEX, self).__init__()
        self.num_entities = num_entities
        self.num_relations = num_relations
        self.embedding_dim = embedding_dim
        self.entity_embeddings_real = nn.Embedding(num_entities, embedding_dim)
        self.entity_embeddings_img = nn.Embedding(num_entities, embedding_dim)
        self.relation_embeddings_real = nn.Embedding(num_relations, embedding_dim)
        self.relation_embeddings_img = nn.Embedding(num_relations, embedding_dim)

    def forward(self, pos_triples, neg_triples):
        pos_heads, pos_relations, pos_tails = pos_triples[:, 0], pos_triples[:, 1], pos_triples[:, 2]
        neg_heads, neg_relations, neg_tails = neg_triples[:, 0], neg_triples[:, 1], neg_triples[:, 2]

        pos_heads_real = self.entity_embeddings_real(pos_heads)
        pos_heads_img = self.entity_embeddings_img(pos_heads)
        pos_relations_real = self.relation_embeddings_real(pos_relations)
        pos_relations_img = self.relation_embeddings_img(pos_relations)
        pos_tails_real = self.entity_embeddings_real(pos_tails)
        pos_tails_img = self.entity_embeddings_img(pos_tails)

        neg_heads_real = self.entity_embeddings_real(neg_heads)
        neg_heads_img = self.entity_embeddings_img(neg_heads)
        neg_relations_real = self.relation_embeddings_real(neg_relations)
        neg_relations_img = self.relation_embeddings_img(neg_relations)
        neg_tails_real = self.entity_embeddings_real(neg_tails)
        neg_tails_img = self.entity_embeddings_img(neg_tails)

        pos_scores = torch.sum(pos_heads_real * pos_relations_real * pos_tails_real, dim=1) + \
                     torch.sum(pos_heads_img * pos_relations_real * pos_tails_img, dim=1) + \
                     torch.sum(pos_heads_real * pos_relations_img * pos_tails_img, dim=1) - \
                     torch.sum(pos_heads_img * pos_relations_img * pos_tails_real, dim=1)

        neg_scores = torch.sum(neg_heads_real * neg_relations_real * neg_tails_real, dim=1) + \
                     torch.sum(neg_heads_img * neg_relations_real * neg_tails_img, dim=1) + \
                     torch.sum(neg_heads_real * neg_relations_img * neg_tails_img, dim=1) - \
                     torch.sum(neg_heads_img * neg_relations_img * neg_tails_real, dim=1)

        loss = -torch.mean(F.logsigmoid(pos_scores) + F.logsigmoid(-neg_scores))

        return loss

该代码实现了一个ComplEX模型，其中包括实体和关系的实部和虚部嵌入。模型的输入是正样本三元组和负样本三元组，输出是损失值。在模型的前向传播过程中，首先将正样本和负样本的实体和关系嵌入提取出来，然后计算正样本和负样本的得分，并计算损失值。最终返回损失值。