python实现trans系列知识图谱嵌入代码

首先，需要安装TransE、TransH、TransR、TransD等知识图谱嵌入模型的Python包，可以使用pip install命令安装相应的包。

以下是TransE知识图谱嵌入模型的Python实现代码：

import numpy as np
import tensorflow as tf

class TransE:
    def __init__(self, entity_num, relation_num, dim, margin, learning_rate):
        self.entity_num = entity_num
        self.relation_num = relation_num
        self.dim = dim
        self.margin = margin
        self.learning_rate = learning_rate

        self.ent_embeddings = tf.get_variable(name="ent_embeddings", shape=[self.entity_num, self.dim], initializer=tf.contrib.layers.xavier_initializer(uniform=False))
        self.rel_embeddings = tf.get_variable(name="rel_embeddings", shape=[self.relation_num, self.dim], initializer=tf.contrib.layers.xavier_initializer(uniform=False))

        self.ent_input = tf.placeholder(dtype=tf.int32, shape=[None])
        self.rel_input = tf.placeholder(dtype=tf.int32, shape=[None])
        self.ent_output = tf.placeholder(dtype=tf.int32, shape=[None])

        self.pos_h = tf.nn.embedding_lookup(self.ent_embeddings, self.ent_input)
        self.pos_t = tf.nn.embedding_lookup(self.ent_embeddings, self.ent_output)
        self.pos_r = tf.nn.embedding_lookup(self.rel_embeddings, self.rel_input)

        self.neg_h = tf.placeholder(dtype=tf.int32, shape=[None])
        self.neg_t = tf.placeholder(dtype=tf.int32, shape=[None])

        self.neg_pos_h = tf.nn.embedding_lookup(self.ent_embeddings, self.neg_h)
        self.neg_pos_t = tf.nn.embedding_lookup(self.ent_embeddings, self.neg_t)

        self.predict = tf.reduce_sum(tf.abs(self.pos_h + self.pos_r - self.pos_t), axis=1, keepdims=True)

        self.loss = tf.reduce_sum(tf.maximum(self.predict - tf.reduce_sum(tf.abs(self.neg_pos_h + self.pos_r - self.pos_t), axis=1, keepdims=True) + self.margin, 0))

        self.train_op = tf.train.GradientDescentOptimizer(self.learning_rate).minimize(self.loss)

        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())

    def train(self, batch_h, batch_t, batch_r, batch_nh, batch_nt):
        _, loss = self.sess.run([self.train_op, self.loss], feed_dict={
            self.ent_input: batch_h,
            self.rel_input: batch_r,
            self.ent_output: batch_t,
            self.neg_h: batch_nh,
            self.neg_t: batch_nt
        })
        return loss

    def test(self, h, t, r):
        return self.sess.run(self.predict, feed_dict={
            self.ent_input: h,
            self.rel_input: r,
            self.ent_output: t
        })

其中，entity_num表示实体的数量，relation_num表示关系的数量，dim表示嵌入维度，margin表示损失函数中的边际值，learning_rate表示学习率。

TransE模型中的实体和关系都被嵌入到dim维空间中，损失函数通过最小化正样本和负样本之间的距离来学习嵌入向量。

batch_h、batch_t和batch_r分别表示一批训练数据中的头实体、尾实体和关系，batch_nh和batch_nt分别表示一批负样本中的头实体和尾实体。

使用以下代码可以创建一个TransE模型的实例并进行训练：

model = TransE(entity_num, relation_num, dim, margin, learning_rate)

for epoch in range(num_epochs):
    for i in range(num_batches):
        batch_h, batch_t, batch_r, batch_nh, batch_nt = generate_batch(batch_size)
        loss = model.train(batch_h, batch_t, batch_r, batch_nh, batch_nt)
    print("Epoch:", epoch, "Loss:", loss)

其中，generate_batch函数用于生成一个batch的训练数据和负样本。训练过程中，每个epoch会迭代num_batches次，每次迭代使用一个batch的数据进行训练。