利用transH得到的知识图谱嵌入向量中应该选用relationNormVectorList作为关系的嵌入向量还是relationHyperVectorList作为关系的嵌入向量
时间: 2024-03-29 12:34:20 浏览: 56
在TransH模型中,relationNormVectorList和relationHyperVectorList都是用来表示关系的嵌入向量的。其中,relationNormVectorList是将关系嵌入到一个普通的向量空间中,而relationHyperVectorList是将关系嵌入到一个超平面上。因此,选用哪个嵌入向量作为关系的表示取决于具体的应用场景和实验效果。
一般来说,relationHyperVectorList可以更好地处理一些复杂的关系,如对称关系、反对称关系等,而relationNormVectorList更适合处理一些简单的关系,如父子关系、兄弟关系等。因此,在实际应用中,可以根据具体的需求和数据集特点选择合适的关系嵌入向量。
相关问题
python实现知识图谱利用TransH嵌入方法嵌入成嵌入向量代码示例
好的,下面是一个简单的 Python 代码示例,演示如何使用 TransH 嵌入方法将知识图谱中的实体和关系嵌入成嵌入向量。
```
import numpy as np
class TransH:
def __init__(self, ent_size, rel_size, margin=1.0, learning_rate=0.01):
self.ent_size = ent_size
self.rel_size = rel_size
self.margin = margin
self.learning_rate = learning_rate
self.ent_embeddings = np.random.rand(ent_size, rel_size)
self.rel_embeddings = np.random.rand(rel_size, ent_size)
self.norms = np.linalg.norm(self.ent_embeddings, axis=1)
self.ent_embeddings = self.ent_embeddings / self.norms[:, None]
def train(self, triplets, epochs=100, batch_size=100):
for epoch in range(epochs):
np.random.shuffle(triplets)
for i in range(0, len(triplets), batch_size):
batch = triplets[i:i+batch_size]
pos_head = self.ent_embeddings[batch[:, 0]]
pos_tail = self.ent_embeddings[batch[:, 1]]
pos_rel = self.rel_embeddings[batch[:, 2]]
neg_head = self.ent_embeddings[batch[:, 3]]
neg_tail = self.ent_embeddings[batch[:, 4]]
neg_rel = self.rel_embeddings[batch[:, 5]]
pos_scores = np.sum(pos_head * pos_rel * pos_tail, axis=1)
neg_scores = np.sum(neg_head * neg_rel * neg_tail, axis=1)
losses = self.margin + neg_scores - pos_scores
losses = np.maximum(losses, 0)
neg_grad_head = losses[:, None] * neg_rel * neg_tail
neg_grad_tail = losses[:, None] * neg_rel * neg_head
pos_grad_head = - losses[:, None] * pos_rel * pos_tail
pos_grad_tail = - losses[:, None] * pos_rel * pos_head
rel_grad = losses[:, None] * (pos_head * pos_tail - neg_head * neg_tail)
self.ent_embeddings[batch[:, 0]] -= self.learning_rate * pos_grad_head
self.ent_embeddings[batch[:, 1]] -= self.learning_rate * pos_grad_tail
self.ent_embeddings[batch[:, 3]] -= self.learning_rate * neg_grad_head
self.ent_embeddings[batch[:, 4]] -= self.learning_rate * neg_grad_tail
self.rel_embeddings[batch[:, 2]] -= self.learning_rate * rel_grad
self.norms = np.linalg.norm(self.ent_embeddings, axis=1)
self.ent_embeddings = self.ent_embeddings / self.norms[:, None]
def predict(self, triplets):
head = self.ent_embeddings[triplets[:, 0]]
tail = self.ent_embeddings[triplets[:, 1]]
rel = self.rel_embeddings[triplets[:, 2]]
scores = np.sum(head * rel * tail, axis=1)
return scores
```
这个示例代码中,我们首先定义了一个 TransH 类,它包含了 TransH 模型的参数和方法。然后,我们在构造函数中初始化了实体和关系的嵌入矩阵,使用随机值进行初始化。在训练方法中,我们首先对训练数据进行随机打乱,然后按照批次进行训练。每个批次中,我们从训练数据中选取了正例和负例,并计算它们的得分。然后,我们根据得分计算损失,并计算每个参数的梯度。最后,我们根据梯度更新参数,并对实体的嵌入向量进行归一化。在预测方法中,我们根据嵌入向量计算每个三元组的得分。
这个示例代码仅仅是一个简单的实现,实际上在 TransH 模型中,还有很多细节需要处理,比如说在计算损失时,需要考虑到正例和负例的相对位置关系,以及在更新参数时,需要对实体的嵌入向量和关系的嵌入向量同时进行更新等等。因此,在实际使用时,还需要进行更多的优化和改进。
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:
```python
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。
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