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详细步骤

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。 ```python 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。