def _define_embed_graph(self):
with tf.name_scope('triple_placeholder'):
self.pos_hs = tf.placeholder(tf.int32, shape=[None])
self.pos_rs = tf.placeholder(tf.int32, shape=[None])
self.pos_ts = tf.placeholder(tf.int32, shape=[None])
self.neg_hs = tf.placeholder(tf.int32, shape=[None])
self.neg_rs = tf.placeholder(tf.int32, shape=[None])
self.neg_ts = tf.placeholder(tf.int32, shape=[None])
self.pos_es = tf.placeholder(tf.int32, shape=[None])
self.pos_as = tf.placeholder(tf.int32, shape=[None])
self.pos_vs = tf.placeholder(tf.int32, shape=[None])
self.neg_es = tf.placeholder(tf.int32, shape=[None])
self.neg_as = tf.placeholder(tf.int32, shape=[None])
self.neg_vs = tf.placeholder(tf.int32, shape=[None])
self.joint_ents = tf.placeholder(tf.int32, shape=[None])
with tf.name_scope('triple_lookup'):
phs = tf.nn.embedding_lookup(self.ent_embeds, self.pos_hs)
prs = tf.nn.embedding_lookup(self.rel_embeds, self.pos_rs)
pts = tf.nn.embedding_lookup(self.ent_embeds, self.pos_ts)
nhs = tf.nn.embedding_lookup(self.ent_embeds, self.neg_hs)
nrs = tf.nn.embedding_lookup(self.rel_embeds, self.neg_rs)
nts = tf.nn.embedding_lookup(self.ent_embeds, self.neg_ts)
pes = tf.nn.embedding_lookup(self.ent_embeds_ce, self.pos_es)
pas = tf.nn.embedding_lookup(self.attr_embeds, self.pos_as)
pvs = tf.nn.embedding_lookup(self.char_embeds, tf.nn.embedding_lookup(self.value_id_char_ids, self.pos_vs))
nes = tf.nn.embedding_lookup(self.ent_embeds_ce, self.neg_es)
nas = tf.nn.embedding_lookup(self.attr_embeds, self.neg_as)
nvs = tf.nn.embedding_lookup(self.char_embeds, tf.nn.embedding_lookup(self.value_id_char_ids, self.neg_vs))
pvs = n_gram_compositional_func(pvs, self.args.literal_len, self.args.batch_size, self.args.dim)
nvs = n_gram_compositional_func(nvs, self.args.literal_len,
self.args.batch_size * self.args.neg_triple_num, self.args.dim)
ents_se = tf.nn.embedding_lookup(self.ent_embeds, self.joint_ents)
ents_ce = tf.nn.embedding_lookup(self.ent_embeds_ce, self.joint_ents)
with tf.name_scope('triple_loss'):
self.triple_loss = get_loss_func(phs, prs, pts, nhs, nrs, nts, self.args)
self.triple_optimizer = generate_optimizer(self.triple_loss, self.args.learning_rate,
opt=self.args.optimizer)
self.triple_loss_ce = get_loss_func(pes, pas, pvs, nes, nas, nvs, self.args)
self.triple_optimizer_ce = generate_optimizer(self.triple_loss_ce, self.args.learning_rate,
opt=self.args.optimizer)
cos_sim = tf.reduce_sum(tf.multiply(ents_se, ents_ce), 1, keep_dims=True)
self.joint_loss = tf.reduce_sum(1 - cos_sim)
self.optimizer_joint = generate_optimizer(self.joint_loss, self.args.learning_rate, opt=self.args.optimizer)
def _define_embed_graph(self):
with tf.name_scope('triple_placeholder'):
self.pos_hs = tf.placeholder(tf.int32, shape=[None])
self.pos_rs = tf.placeholder(tf.int32, shape=[None])
self.pos_ts = tf.placeholder(tf.int32, shape=[None])
self.neg_hs = tf.placeholder(tf.int32, shape=[None])
self.neg_rs = tf.placeholder(tf.int32, shape=[None])
self.neg_ts = tf.placeholder(tf.int32, shape=[None])
with tf.name_scope('triple_lookup'):
phe = tf.nn.embedding_lookup(self.ent_embeds, self.pos_hs)
pte = tf.nn.embedding_lookup(self.ent_embeds, self.pos_ts)
pre = tf.nn.embedding_lookup(self.rel_embeds, self.pos_rs)
pht = tf.nn.embedding_lookup(self.ent_transfer, self.pos_hs)
ptt = tf.nn.embedding_lookup(self.ent_transfer, self.pos_ts)
prt = tf.nn.embedding_lookup(self.rel_transfer, self.pos_rs)
nhe = tf.nn.embedding_lookup(self.ent_embeds, self.neg_hs)
nte = tf.nn.embedding_lookup(self.ent_embeds, self.neg_ts)
nre = tf.nn.embedding_lookup(self.rel_embeds, self.neg_rs)
nht = tf.nn.embedding_lookup(self.ent_transfer, self.neg_hs)
ntt = tf.nn.embedding_lookup(self.ent_transfer, self.neg_ts)
nrt = tf.nn.embedding_lookup(self.rel_transfer, self.neg_rs)
with tf.name_scope('projection'):
phe = self._calc(phe, pht, prt)
pte = self._calc(pte, ptt, prt)
nhe = self._calc(nhe, nht, nrt)
nte = self._calc(nte, ntt, nrt)
with tf.name_scope('triple_loss'):
self.triple_loss = get_loss_func(phe, pre, pte, nhe, nre, nte, self.args)
self.triple_optimizer = generate_optimizer(self.triple_loss, self.args.learning_rate,
opt=self.args.optimizer)
def _define_embed_graph(self):
with tf.name_scope('triple_placeholder'):
self.pos_hs = tf.placeholder(tf.int32, shape=[None])
self.pos_rs = tf.placeholder(tf.int32, shape=[None])
self.pos_ts = tf.placeholder(tf.int32, shape=[None])
self.neg_hs = tf.placeholder(tf.int32, shape=[None])
self.neg_rs = tf.placeholder(tf.int32, shape=[None])
self.neg_ts = tf.placeholder(tf.int32, shape=[None])
self.aligned_ents1 = tf.placeholder(tf.int32, shape=[None])
self.aligned_ents2 = tf.placeholder(tf.int32, shape=[None])
with tf.name_scope('triple_lookup'):
phs = tf.nn.embedding_lookup(self.ent_embeds, self.pos_hs)
prs = tf.nn.embedding_lookup(self.rel_embeds, self.pos_rs)
pts = tf.nn.embedding_lookup(self.ent_embeds, self.pos_ts)
nhs = tf.nn.embedding_lookup(self.ent_embeds, self.neg_hs)
nrs = tf.nn.embedding_lookup(self.rel_embeds, self.neg_rs)
nts = tf.nn.embedding_lookup(self.ent_embeds, self.neg_ts)
ents1 = tf.nn.embedding_lookup(self.ent_embeds, self.aligned_ents1)
ents2 = tf.nn.embedding_lookup(self.ent_embeds, self.aligned_ents2)
with tf.name_scope('triple_loss'):
self.triple_loss = get_loss_func(phs, prs, pts, nhs, nrs, nts,
self.args)
self.triple_optimizer = generate_optimizer(self.triple_loss,
self.args.learning_rate,
opt=self.args.optimizer)
with tf.name_scope('align_loss'):
self.align_loss = tf.reduce_sum(
tf.reduce_sum(tf.pow(ents1 - ents2, 2), 1))
self.align_optimizer = generate_optimizer(self.align_loss,
self.args.learning_rate,
opt=self.args.optimizer)
def _define_embed_graph(self):
with tf.name_scope('triple_placeholder'):
self.pos_hs = tf.placeholder(tf.int32, shape=[None])
self.pos_rs = tf.placeholder(tf.int32, shape=[None])
self.pos_ts = tf.placeholder(tf.int32, shape=[None])
self.neg_hs = tf.placeholder(tf.int32, shape=[None])
self.neg_rs = tf.placeholder(tf.int32, shape=[None])
self.neg_ts = tf.placeholder(tf.int32, shape=[None])
with tf.name_scope('triple_lookup'):
phs = tf.nn.embedding_lookup(self.ent_embeds, self.pos_hs)
prs = tf.nn.embedding_lookup(self.rel_embeds, self.pos_rs)
pts = tf.nn.embedding_lookup(self.ent_embeds, self.pos_ts)
nhs = tf.nn.embedding_lookup(self.ent_embeds, self.neg_hs)
nrs = tf.nn.embedding_lookup(self.rel_embeds, self.neg_rs)
nts = tf.nn.embedding_lookup(self.ent_embeds, self.neg_ts)
with tf.name_scope('triple_loss'):
self.triple_loss = get_loss_func(phs, prs, pts, nhs, nrs, nts, self.args)
self.triple_optimizer = generate_optimizer(self.triple_loss, self.args.learning_rate,
opt=self.args.optimizer)
with tf.name_scope('seed_links_placeholder'):
self.labeled_entities1 = tf.placeholder(tf.int32, shape=[None])
self.labeled_entities2 = tf.placeholder(tf.int32, shape=[None])
self.unlabeled_entities1 = tf.placeholder(tf.int32, shape=[None])
self.unlabeled_entities2 = tf.placeholder(tf.int32, shape=[None])
with tf.name_scope('seed_links_lookup'):
labeled_embeds1 = tf.nn.embedding_lookup(self.ent_embeds, self.labeled_entities1)
labeled_embeds2 = tf.nn.embedding_lookup(self.ent_embeds, self.labeled_entities2)
unlabeled_embeds1 = tf.nn.embedding_lookup(self.ent_embeds, self.unlabeled_entities1)
unlabeled_embeds2 = tf.nn.embedding_lookup(self.ent_embeds, self.unlabeled_entities2)
with tf.name_scope('sup_mapping_loss'):
mapped_12 = tf.nn.l2_normalize(tf.matmul(labeled_embeds1, self.mapping_mat_1))
mapped_21 = tf.nn.l2_normalize(tf.matmul(labeled_embeds2, self.mapping_mat_2))
map_loss_12 = tf.reduce_sum(tf.reduce_sum(tf.pow(labeled_embeds2 - mapped_12, 2), 1))
map_loss_21 = tf.reduce_sum(tf.reduce_sum(tf.pow(labeled_embeds1 - mapped_21, 2), 1))
with tf.name_scope('semi_sup_mapping_loss'):
semi_mapped_121 = tf.nn.l2_normalize(tf.matmul(tf.matmul(unlabeled_embeds1, self.mapping_mat_1),
self.mapping_mat_2))
semi_mapped_212 = tf.nn.l2_normalize(tf.matmul(tf.matmul(unlabeled_embeds2, self.mapping_mat_2),
self.mapping_mat_1))
map_loss_11 = tf.reduce_sum(tf.reduce_sum(tf.pow(unlabeled_embeds1 - semi_mapped_121, 2), 1))
map_loss_22 = tf.reduce_sum(tf.reduce_sum(tf.pow(unlabeled_embeds2 - semi_mapped_212, 2), 1))
self.mapping_loss = self.args.alpha_1 * (map_loss_12 + map_loss_21) + \
self.args.alpha_2 * (map_loss_11 + map_loss_22)
self.mapping_optimizer = generate_optimizer(self.mapping_loss, self.args.learning_rate,
opt=self.args.optimizer)
def _define_embed_graph(self):
with tf.name_scope('triple_placeholder'):
self.pos_hs = tf.placeholder(tf.int32, shape=[None])
self.pos_rs = tf.placeholder(tf.int32, shape=[None])
self.pos_ts = tf.placeholder(tf.int32, shape=[None])
self.neg_hs = tf.placeholder(tf.int32, shape=[None])
self.neg_rs = tf.placeholder(tf.int32, shape=[None])
self.neg_ts = tf.placeholder(tf.int32, shape=[None])
with tf.name_scope('triple_lookup'):
phs = tf.reshape(
tf.nn.embedding_lookup(self.ent_embeds, self.pos_hs),
[-1, self.args.dim, 1])
prs = tf.nn.embedding_lookup(self.rel_embeds, self.pos_rs)
pts = tf.reshape(
tf.nn.embedding_lookup(self.ent_embeds, self.pos_ts),
[-1, self.args.dim, 1])
nhs = tf.reshape(
tf.nn.embedding_lookup(self.ent_embeds, self.neg_hs),
[-1, self.args.dim, 1])
nrs = tf.nn.embedding_lookup(self.rel_embeds, self.neg_rs)
nts = tf.reshape(
tf.nn.embedding_lookup(self.ent_embeds, self.neg_ts),
[-1, self.args.dim, 1])
p_matrix = tf.reshape(
tf.nn.embedding_lookup(self.rel_matrix, self.pos_rs),
[-1, self.args.dim, self.args.dim])
n_matrix = tf.reshape(
tf.nn.embedding_lookup(self.rel_matrix, self.neg_rs),
[-1, self.args.dim, self.args.dim])
phs = tf.reshape(tf.matmul(p_matrix, phs), [-1, self.args.dim])
pts = tf.reshape(tf.matmul(p_matrix, pts), [-1, self.args.dim])
phs = tf.nn.l2_normalize(phs, 1)
pts = tf.nn.l2_normalize(pts, 1)
nhs = tf.reshape(tf.matmul(n_matrix, nhs), [-1, self.args.dim])
nts = tf.reshape(tf.matmul(n_matrix, nts), [-1, self.args.dim])
nhs = tf.nn.l2_normalize(nhs, 1)
nts = tf.nn.l2_normalize(nts, 1)
with tf.name_scope('triple_loss'):
self.triple_loss = get_loss_func(phs, prs, pts, nhs, nrs, nts,
self.args)
self.triple_optimizer = generate_optimizer(self.triple_loss,
self.args.learning_rate,
opt=self.args.optimizer)