知识图谱的实体含有属性应该如何嵌入成向量代码

嵌入实体和属性成向量的方法可以分为两类：基于统计的方法和基于神经网络的方法。下面分别介绍这两种方法的实现代码。

### 基于统计的方法

#### 1. Word2Vec

from gensim.models import Word2Vec

# 构建语料库
sentences = [["entity1", "property1", "value1"],
             ["entity2", "property2", "value2", "value3"],
             ["entity3", "property1", "value4"]]

# 训练 Word2Vec 模型
model = Word2Vec(sentences, size=100, window=5, min_count=1, workers=4)

# 获取实体和属性的向量表示
entity_vec = model.wv["entity1"]
property_vec = model.wv["property1"]

#### 2. GloVe

from glove import Corpus, Glove

# 构建语料库
sentences = [["entity1", "property1", "value1"],
             ["entity2", "property2", "value2", "value3"],
             ["entity3", "property1", "value4"]]

# 创建 Corpus 对象并训练 GloVe 模型
corpus = Corpus()
corpus.fit(sentences, window=5)
glove = Glove(no_components=100, learning_rate=0.05)
glove.fit(corpus.matrix, epochs=30, no_threads=4, verbose=True)

# 获取实体和属性的向量表示
entity_vec = glove.word_vectors[glove.dictionary["entity1"]]
property_vec = glove.word_vectors[glove.dictionary["property1"]]

### 基于神经网络的方法

#### 1. TransE

import torch
import torch.nn as nn

# 定义 TransE 模型
class TransE(nn.Module):
    def __init__(self, entity_num, property_num, embedding_dim):
        super(TransE, self).__init__()
        self.entity_embeddings = nn.Embedding(entity_num, embedding_dim)
        self.property_embeddings = nn.Embedding(property_num, embedding_dim)
        nn.init.xavier_uniform_(self.entity_embeddings.weight.data)
        nn.init.xavier_uniform_(self.property_embeddings.weight.data)

    def forward(self, head, relation, tail):
        head_embedding = self.entity_embeddings(head)
        relation_embedding = self.property_embeddings(relation)
        tail_embedding = self.entity_embeddings(tail)
        score = torch.norm(head_embedding + relation_embedding - tail_embedding, p=2, dim=1)
        return score

# 定义训练数据
triplets = [(0, 0, 1), (1, 1, 2), (2, 0, 3)]
entity_num = 4
property_num = 2

# 训练 TransE 模型
model = TransE(entity_num, property_num, embedding_dim=50)
criterion = nn.MarginRankingLoss(margin=1.0)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
for epoch in range(100):
    for pos_triplet in triplets:
        pos_head, pos_relation, pos_tail = pos_triplet
        neg_triplet = (pos_head, 1 - pos_relation, pos_tail)
        pos_head, pos_relation, pos_tail = torch.tensor([pos_head]), torch.tensor([pos_relation]), torch.tensor([pos_tail])
        neg_head, neg_relation, neg_tail = torch.tensor([neg_triplet[0]]), torch.tensor([neg_triplet[1]]), torch.tensor([neg_triplet[2]])
        pos_score = model(pos_head, pos_relation, pos_tail)
        neg_score = model(neg_head, neg_relation, neg_tail)
        loss = criterion(pos_score, neg_score, torch.tensor([1.0]))
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# 获取实体和属性的向量表示
entity_vec = model.entity_embeddings(torch.tensor([0]))[0].detach().numpy()
property_vec = model.property_embeddings(torch.tensor([0]))[0].detach().numpy()

#### 2. ConvE

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

# 定义 ConvE 模型
class ConvE(nn.Module):
    def __init__(self, entity_num, property_num, embedding_dim, feature_map_size, dropout_rate):
        super(ConvE, self).__init__()
        self.entity_embeddings = nn.Embedding(entity_num, embedding_dim)
        self.property_embeddings = nn.Embedding(property_num, embedding_dim)
        self.conv = nn.Conv2d(1, feature_map_size, (3, 3), 1, 0, bias=True)
        self.fc = nn.Linear(feature_map_size * (embedding_dim - 2) * (embedding_dim - 2), embedding_dim)
        self.dropout = nn.Dropout(dropout_rate)
        nn.init.xavier_uniform_(self.entity_embeddings.weight.data)
        nn.init.xavier_uniform_(self.property_embeddings.weight.data)
        nn.init.xavier_uniform_(self.fc.weight.data)

    def forward(self, head, relation, tail):
        batch_size = head.size(0)
        head_embedding = self.entity_embeddings(head)
        relation_embedding = self.property_embeddings(relation)
        tail_embedding = self.entity_embeddings(tail)
        x = torch.cat([head_embedding, relation_embedding], dim=2).unsqueeze(1)
        x = F.relu(self.conv(x)).view(batch_size, -1)
        x = self.fc(x)
        x = self.dropout(x)
        score = torch.norm(x - tail_embedding, p=2, dim=1)
        return score

# 定义训练数据
triplets = [(0, 0, 1), (1, 1, 2), (2, 0, 3)]
entity_num = 4
property_num = 2

# 训练 ConvE 模型
model = ConvE(entity_num, property_num, embedding_dim=50, feature_map_size=10, dropout_rate=0.2)
criterion = nn.MarginRankingLoss(margin=1.0)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
for epoch in range(100):
    for pos_triplet in triplets:
        pos_head, pos_relation, pos_tail = pos_triplet
        neg_triplet = (pos_head, 1 - pos_relation, pos_tail)
        pos_head, pos_relation, pos_tail = torch.tensor([pos_head]), torch.tensor([pos_relation]), torch.tensor([pos_tail])
        neg_head, neg_relation, neg_tail = torch.tensor([neg_triplet[0]]), torch.tensor([neg_triplet[1]]), torch.tensor([neg_triplet[2]])
        pos_score = model(pos_head, pos_relation, pos_tail)
        neg_score = model(neg_head, neg_relation, neg_tail)
        loss = criterion(pos_score, neg_score, torch.tensor([1.0]))
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# 获取实体和属性的向量表示
entity_vec = model.entity_embeddings(torch.tensor([0]))[0].detach().numpy()
property_vec = model.property_embeddings(torch.tensor([0]))[0].detach().numpy()