python实现将neo4j的知识图谱利用TransH嵌入方法转换成嵌入向量

以下是使用Python将Neo4j的知识图谱转换为嵌入向量的示例代码，利用的是TransH方法：

from py2neo import Graph
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader

# Neo4j数据库连接信息
uri = "bolt://localhost:7687"
user = "neo4j"
password = "password"

# TransH嵌入向量设置
embedding_dim = 50  # 嵌入向量维度
margin = 1.0  # 损失函数中的边界值
learning_rate = 0.01  # 学习率
num_epochs = 100  # 迭代次数

# Neo4j数据库操作
class Neo4jDB:
    def __init__(self, uri, user, password):
        self.graph = Graph(uri, auth=(user, password))

    def close(self):
        self.graph.close()

    def get_all_triples(self):
        query = """
        MATCH (h)-[r]->(t)
        RETURN id(h) AS h_id, id(t) AS t_id, type(r) AS r_type
        """
        result = self.graph.run(query)
        triples = [(row["h_id"], row["t_id"], row["r_type"]) for row in result]
        return triples

# TransH嵌入向量模型
class TransHModel(torch.nn.Module):
    def __init__(self, num_entities, num_relations, embedding_dim):
        super(TransHModel, self).__init__()
        self.num_entities = num_entities
        self.num_relations = num_relations
        self.embedding_dim = embedding_dim
        
        # 实体、关系、嵌入向量初始化
        self.entity_embedding = torch.nn.Embedding(num_entities, embedding_dim)
        self.relation_embedding = torch.nn.Embedding(num_relations, embedding_dim)
        self.normal_vector = torch.nn.Embedding(num_relations, embedding_dim)
        
        # 参数初始化
        torch.nn.init.xavier_uniform_(self.entity_embedding.weight.data)
        torch.nn.init.xavier_uniform_(self.relation_embedding.weight.data)
        torch.nn.init.xavier_uniform_(self.normal_vector.weight.data)

    def forward(self, head, tail, relation):
        # 获取实体、关系、嵌入向量
        entity_emb = self.entity_embedding(torch.LongTensor(range(self.num_entities)).to(device))
        relation_emb = self.relation_embedding(torch.LongTensor(range(self.num_relations)).to(device))
        normal_vector_emb = self.normal_vector(torch.LongTensor(range(self.num_relations)).to(device))
        
        # 计算头实体和尾实体在关系空间中的表示
        head_emb = torch.index_select(entity_emb, 0, head)
        tail_emb = torch.index_select(entity_emb, 0, tail)
        relation_emb = torch.index_select(relation_emb, 0, relation)
        normal_vector_emb = torch.index_select(normal_vector_emb, 0, relation)
        head_proj = head_emb - torch.sum(head_emb * normal_vector_emb, dim=1, keepdim=True) * normal_vector_emb
        tail_proj = tail_emb - torch.sum(tail_emb * normal_vector_emb, dim=1, keepdim=True) * normal_vector_emb
        
        # 计算距离和损失
        distance = torch.norm(head_proj + relation_emb - tail_proj, p=2, dim=1)
        loss = torch.mean(torch.relu(distance - margin))
        return loss

# 数据集类
class TripleDataset(Dataset):
    def __init__(self, triples, num_entities, num_relations):
        self.triples = triples
        self.num_entities = num_entities
        self.num_relations = num_relations

    def __len__(self):
        return len(self.triples)

    def __getitem__(self, idx):
        head, tail, relation = self.triples[idx]
        return head, tail, relation

# 加载数据集
db = Neo4jDB(uri, user, password)
triples = db.get_all_triples()
num_entities = len(set([triple[0] for triple in triples] + [triple[1] for triple in triples]))
num_relations = len(set([triple[2] for triple in triples]))
train_dataset = TripleDataset(triples, num_entities, num_relations)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)

# 模型和优化器
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TransHModel(num_entities, num_relations, embedding_dim).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 模型训练
for epoch in range(num_epochs):
    total_loss = 0.0
    for batch_idx, (head, tail, relation) in enumerate(train_loader):
        head, tail, relation = head.to(device), tail.to(device), relation.to(device)
        optimizer.zero_grad()
        loss = model(head, tail, relation)
        loss.backward()
        optimizer.step()
        total_loss += loss.item()
    print("Epoch {}, Loss {:.4f}".format(epoch+1, total_loss/len(train_loader)))

# 提取嵌入向量
entity_emb = model.entity_embedding(torch.LongTensor(range(num_entities)).to(device)).cpu().detach().numpy()
relation_emb = model.relation_embedding(torch.LongTensor(range(num_relations)).to(device)).cpu().detach().numpy()
normal_vector_emb = model.normal_vector(torch.LongTensor(range(num_relations)).to(device)).cpu().detach().numpy()

# 保存嵌入向量
np.save("entity_embedding.npy", entity_emb)
np.save("relation_embedding.npy", relation_emb)
np.save("normal_vector_embedding.npy", normal_vector_emb)

# 关闭数据库连接
db.close()

其中，`Neo4jDB`类封装了Neo4j数据库的连接和操作，其中`get_all_triples`方法用于获取所有三元组数据。`TransHModel`类定义了TransH模型的架构和前向传播过程，其中包括实体、关系和嵌入向量的初始化、距离计算和损失计算。`TripleDataset`类定义了数据集的格式和获取方式。

在代码中，首先通过`get_all_triples`方法获取所有三元组数据，并根据实体和关系的数量初始化模型。然后使用PyTorch的`DataLoader`将三元组数据转换为批次，并在循环中使用`model`计算损失并更新参数。在训练完成后，通过`model`提取实体、关系和嵌入向量，并保存到文件中。