python实现将neo4j的知识图谱利用TransH嵌入方法转换成嵌入向量代码并附有详细注释

好的，我可以为您提供Python实现将Neo4j的知识图谱利用TransH嵌入方法转换成嵌入向量的代码，并附有详细注释。在提供代码之前，需要先安装PyTorch和py2neo模块。

import torch
from torch.utils.data import DataLoader
from py2neo import Graph, Node, Relationship

# 定义TransH模型
class TransH(torch.nn.Module):
    def __init__(self, ent_num, rel_num, dim, margin):
        super(TransH, self).__init__()
        self.ent_num = ent_num
        self.rel_num = rel_num
        self.dim = dim
        self.margin = margin
        self.ent_embedding = torch.nn.Embedding(self.ent_num, self.dim)
        self.rel_embedding = torch.nn.Embedding(self.rel_num, self.dim)
        self.norm_vector = torch.nn.Embedding(self.rel_num, self.dim)

    def _calc(self, h, t, r):
        h = h.view(-1, self.dim, 1)
        t = t.view(-1, self.dim, 1)
        r = r.view(-1, self.dim, 1)
        norm = torch.norm(r, p=2, dim=1, keepdim=True)
        norm_r = r / norm
        norm_h = torch.matmul(h, norm_r.transpose(1,2))
        norm_t = torch.matmul(t, norm_r.transpose(1,2))
        score = torch.norm(norm_h + r - norm_t, p=2, dim=1)
        return score

    def forward(self, pos_h, pos_t, pos_r, neg_h, neg_t, neg_r):
        pos_score = self._calc(pos_h, pos_t, pos_r)
        neg_score = self._calc(neg_h, neg_t, neg_r)
        loss_func = torch.nn.MarginRankingLoss(margin=self.margin)
        y = torch.Tensor([-1])
        loss = loss_func(pos_score, neg_score, y)
        return loss

    def ent_embeddings(self):
        return self.ent_embedding.weight.detach().cpu().numpy()

# 加载知识图谱数据
class KnowledgeGraphDataLoader(DataLoader):
    def __init__(self, graph, batch_size, num_workers):
        self.graph = graph
        self.batch_size = batch_size
        self.num_workers = num_workers
        self.ent2id = {}
        self.rel2id = {}
        self.id2ent = {}
        self.id2rel = {}
        self.train_triples = []
        self.dev_triples = []
        self.test_triples = []
        self.load_data()

    # 加载数据
    def load_data(self):
        query = "MATCH (h)-[r]->(t) RETURN id(h), id(t), type(r)"
        result = self.graph.run(query)
        for row in result:
            h, t, r = row
            if h not in self.ent2id:
                self.ent2id[h] = len(self.ent2id)
                self.id2ent[self.ent2id[h]] = h
            if t not in self.ent2id:
                self.ent2id[t] = len(self.ent2id)
                self.id2ent[self.ent2id[t]] = t
            if r not in self.rel2id:
                self.rel2id[r] = len(self.rel2id)
                self.id2rel[self.rel2id[r]] = r
            self.train_triples.append((self.ent2id[h], self.ent2id[t], self.rel2id[r]))

    # 获取训练数据
    def get_train_data(self):
        return self.train_triples

    # 获取实体数量
    def get_ent_num(self):
        return len(self.ent2id)

    # 获取关系数量
    def get_rel_num(self):
        return len(self.rel2id)

    # 获取实体ID
    def get_ent_id(self, ent):
        return self.ent2id[ent]

    # 获取关系ID
    def get_rel_id(self, rel):
        return self.rel2id[rel]

    # 获取实体
    def get_ent(self, ent_id):
        return self.id2ent[ent_id]

    # 获取关系
    def get_rel(self, rel_id):
        return self.id2rel[rel_id]

# 训练TransH模型
def train_transh(graph, dim=50, margin=1.0, lr=0.01, batch_size=1000, epochs=500, num_workers=8):
    data_loader = KnowledgeGraphDataLoader(graph, batch_size, num_workers)
    ent_num = data_loader.get_ent_num()
    rel_num = data_loader.get_rel_num()
    transh = TransH(ent_num, rel_num, dim, margin)
    optimizer = torch.optim.SGD(transh.parameters(), lr=lr)
    transh.train()
    for epoch in range(epochs):
        data_iter = DataLoader(data_loader, batch_size=batch_size, shuffle=True, num_workers=num_workers)
        for batch in data_iter:
            pos_h, pos_t, pos_r = torch.LongTensor(batch[:, 0]), torch.LongTensor(batch[:, 1]), torch.LongTensor(batch[:, 2])
            neg_h, neg_t, neg_r = data_loader.get_corrupted_triples(pos_h, pos_t, pos_r)
            loss = transh(pos_h, pos_t, pos_r, neg_h, neg_t, neg_r)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, epochs, loss.item()))
    return transh.ent_embeddings()

# 连接Neo4j数据库
graph = Graph("bolt://localhost:7687", auth=('neo4j', 'password'))

# 训练TransH模型
ent_embeddings = train_transh(graph, dim=50, margin=1.0, lr=0.01, batch_size=1000, epochs=500, num_workers=8)

# 保存实体嵌入向量
for ent_id in range(len(ent_embeddings)):
    ent = data_loader.get_ent(ent_id)
    node = Node("Entity", name=ent, embedding=ent_embeddings[ent_id])
    graph.create(node)

以上代码实现了将Neo4j的知识图谱利用TransH嵌入方法转换成嵌入向量，并将实体嵌入向量保存到Neo4j数据库中。其中，TransH模型的训练过程采用随机梯度下降算法，优化器使用SGD，损失函数采用MarginRankingLoss，训练过程中使用了多线程加载数据。由于每个实体的嵌入向量是一个向量，因此使用了Neo4j的属性来保存实体嵌入向量。