---

图谱问答

图谱问答有很多种情况，例如根据实体和关系查询尾实体，或者根据实体查询关系，甚至还会出现多跳的情况，不同的情况采用的方法略有不同，我们先来看最简单的情况，根据头实体和关系查询尾实体。

1、找到实体与关系，可以采用BIO的形式做NER，也可以直接使用分类的方法
2、实体链接，如果遇到相同名字的实体，需要做一个消歧

NER

目前的NER的方式很多，基本的结构都是encoder+crf层

ac自动机

1、构建前缀树
2、给前缀树加上fail指针
节点i的fail指针，如果在第一层，则指向root节点，其它情况指向其父节点的fail指针指向的节点的相同节点

有如下的几个模式串：she he say shr her
匹配串：yasherhs

实体链接

实体链接包括两个步骤：
Candidate Entity Generation、Entity Disambiguation

找到候选实体后，下一步就是实体消歧

实体消歧

实体消歧，这里我们使用的是匹配的方法：
1、使用孪生网络，计算相似度
2、对问题和候选集做embedding，计算余弦相似度

多跳问答

neo4j_graph执行流程

1、先执行import_data.py脚本，把company_data下面的数据导入到neo4j

2、执行gnn/saint.py脚本进行节点分类

3、company.csv文件是每个节点的属性

结构图

company_data

截图举几个例子（ps：数据为虚假，作为学习使用）：

代码与数据

先启动neo4j图数据库

操作流程：WIN+R，cmd，neo4j.bat concole

import_data

import os
from py2neo import Node, Subgraph, Graph, Relationship, NodeMatcher
from tqdm import tqdm
import pandas as pd
import numpy as np#graph = Graph("http://127.0.0.1:7474", auth=("neo4j", "qwer"))#graph = Graph("http://127.0.0.1:7474", auth=("neo4j", "qwer"))#uri = 'bolt://localhost:7687'
#graph = Graph(uri, auth=("neo4j", "password"), port= 7687, secure=True)#uri = uri = 'http://localhost:7687'
#graph = Graph(uri, auth=("neo4j", "qwer"), port= 7687, secure=True, name= "StellarGraph")import py2neo
default_host = os.environ.get("STELLARGRAPH_NEO4J_HOST")# Create the Neo4j Graph database object; the arguments can be edited to specify location and authenticationgraph = py2neo.Graph(host=default_host, port=7687, user='neo4j', password='qwer')def import_company():df = pd.read_csv('company_data/公司.csv')eid = df['eid'].valuesname = df['companyname'].valuesnodes = []data = list(zip(eid, name))for eid, name in tqdm(data):profit = np.random.randint(100000, 100000000, 1)[0]node = Node('company', name=name, profit=int(profit), eid=eid)nodes.append(node)graph.create(Subgraph(nodes))def import_person():df = pd.read_csv('company_data/人物.csv')pid = df['personcode'].valuesname = df['personname'].valuesnodes = []data = list(zip(pid, name))for eid, name in tqdm(data):age = np.random.randint(20, 70, 1)[0]node = Node('person', name=name, age=int(age), pid=str(eid))nodes.append(node)graph.create(Subgraph(nodes))def import_industry():df = pd.read_csv('company_data/行业.csv')names = df['orgtype'].valuesnodes = []for name in tqdm(names):node = Node('industry', name=name)nodes.append(node)graph.create(Subgraph(nodes))def import_assign():df = pd.read_csv('company_data/分红.csv')names = df['schemetype'].valuesnodes = []for name in tqdm(names):node = Node('assign', name=name)nodes.append(node)graph.create(Subgraph(nodes))def import_violations():df = pd.read_csv('company_data/违规类型.csv')names = df['gooltype'].valuesnodes = []for name in tqdm(names):node = Node('violations', name=name)nodes.append(node)graph.create(Subgraph(nodes))def import_bond():df = pd.read_csv('company_data/债券类型.csv')names = df['securitytype'].valuesnodes = []for name in tqdm(names):node = Node('bond', name=name)nodes.append(node)graph.create(Subgraph(nodes))# def import_dishonesty():
#     node = Node('dishonesty', name='失信')
#     graph.create(node)def import_relation():df = pd.read_csv('company_data/公司-人物.csv')matcher = NodeMatcher(graph)eid = df['eid'].valuespid = df['pid'].valuespost = df['post'].valuesrelations = []data = list(zip(eid, pid, post))for e, p, po in tqdm(data):company = matcher.match('company', eid=e).first()person = matcher.match('person', pid=str(p)).first()if company is not None and person is not None:relations.append(Relationship(company, po, person))graph.create(Subgraph(relationships=relations))print('import company-person relation succeeded')df = pd.read_csv('company_data/公司-行业.csv')matcher = NodeMatcher(graph)eid = df['eid'].valuesname = df['industry'].valuesrelations = []data = list(zip(eid, name))for e, n in tqdm(data):company = matcher.match('company', eid=e).first()industry = matcher.match('industry', name=str(n)).first()if company is not None and industry is not None:relations.append(Relationship(company, '行业类型', industry))graph.create(Subgraph(relationships=relations))print('import company-industry relation succeeded')df = pd.read_csv('company_data/公司-分红.csv')matcher = NodeMatcher(graph)eid = df['eid'].valuesname = df['assign'].valuesrelations = []data = list(zip(eid, name))for e, n in tqdm(data):company = matcher.match('company', eid=e).first()assign = matcher.match('assign', name=str(n)).first()if company is not None and assign is not None:relations.append(Relationship(company, '分红方式', assign))graph.create(Subgraph(relationships=relations))print('import company-assign relation succeeded')df = pd.read_csv('company_data/公司-违规.csv')matcher = NodeMatcher(graph)eid = df['eid'].valuesname = df['violations'].valuesrelations = []data = list(zip(eid, name))for e, n in tqdm(data):company = matcher.match('company', eid=e).first()violations = matcher.match('violations', name=str(n)).first()if company is not None and violations is not None:relations.append(Relationship(company, '违规类型', violations))graph.create(Subgraph(relationships=relations))print('import company-violations relation succeeded')df = pd.read_csv('company_data/公司-债券.csv')matcher = NodeMatcher(graph)eid = df['eid'].valuesname = df['bond'].valuesrelations = []data = list(zip(eid, name))for e, n in tqdm(data):company = matcher.match('company', eid=e).first()bond = matcher.match('bond', name=str(n)).first()if company is not None and bond is not None:relations.append(Relationship(company, '债券类型', bond))graph.create(Subgraph(relationships=relations))print('import company-bond relation succeeded')# df = pd.read_csv('company_data/公司-失信.csv')# matcher = NodeMatcher(graph)# eid = df['eid'].values# rel = df['dishonesty'].values# relations = []# data = list(zip(eid, rel))# for e, r in tqdm(data):#     company = matcher.match('company', eid=e).first()#     dishonesty = matcher.match('dishonesty', name='失信').first()#     if company is not None and dishonesty is not None:#         if pd.notna(r):#             if int(r) == 0:#                 relations.append(Relationship(company, '无', dishonesty))#             elif int(r) == 1:#                 relations.append(Relationship(company, '有', dishonesty))## graph.create(Subgraph(relationships=relations))# print('import company-dishonesty relation succeeded')def import_company_relation():df = pd.read_csv('company_data/公司-供应商.csv')matcher = NodeMatcher(graph)eid1 = df['eid1'].valueseid2 = df['eid2'].valuesrelations = []data = list(zip(eid1, eid2))for e1, e2 in tqdm(data):if pd.notna(e1) and pd.notna(e2) and e1 != e2:company1 = matcher.match('company', eid=e1).first()company2 = matcher.match('company', eid=e2).first()if company1 is not None and company2 is not None:relations.append(Relationship(company1, '供应商', company2))graph.create(Subgraph(relationships=relations))print('import company-supplier relation succeeded')df = pd.read_csv('company_data/公司-担保.csv')matcher = NodeMatcher(graph)eid1 = df['eid1'].valueseid2 = df['eid2'].valuesrelations = []data = list(zip(eid1, eid2))for e1, e2 in tqdm(data):if pd.notna(e1) and pd.notna(e2) and e1 != e2:company1 = matcher.match('company', eid=e1).first()company2 = matcher.match('company', eid=e2).first()if company1 is not None and company2 is not None:relations.append(Relationship(company1, '担保', company2))graph.create(Subgraph(relationships=relations))print('import company-guarantee relation succeeded')df = pd.read_csv('company_data/公司-客户.csv')matcher = NodeMatcher(graph)eid1 = df['eid1'].valueseid2 = df['eid2'].valuesrelations = []data = list(zip(eid1, eid2))for e1, e2 in tqdm(data):if pd.notna(e1) and pd.notna(e2):company1 = matcher.match('company', eid=e1).first()company2 = matcher.match('company', eid=e2).first()if company1 is not None and company2 is not None:relations.append(Relationship(company1, '客户', company2))graph.create(Subgraph(relationships=relations))print('import company-customer relation succeeded')def delete_relation():cypher = 'match ()-[r]-() delete r'graph.run(cypher)def delete_node():cypher = 'match (n) delete n'graph.run(cypher)def import_data():import_company()import_company_relation()import_person()import_industry()import_assign()import_violations()import_bond()# import_dishonesty()import_relation()def delete_data():delete_relation()delete_node()print('delete data succeeded')if __name__ == '__main__':profit = np.random.randint(100000, 100000000, 10).tolist()delete_data()import_data()

create_question_data

from py2neo import Graph
import numpy as np
import pandas as pdgraph = Graph("http://localhost:7474", auth=("neo4j", "qwer"))# import os
# import py2neo
# default_host = os.environ.get("STELLARGRAPH_NEO4J_HOST")
# graph = py2neo.Graph(host=default_host, port=7687, user='neo4j', password='qwer')def create_attribute_question():company = graph.run('MATCH (n:company) RETURN n.name as name').to_ndarray()person = graph.run('MATCH (n:person) RETURN n.name as name').to_ndarray()questions = []for c in company:c = c[0].strip()question = f"{c}的收益"questions.append(question)question = f"{c}的收入"questions.append(question)for p in person:p = p[0].strip()question = f"{p}的年龄是几岁"questions.append(question)question = f"{p}多大"questions.append(question)question = f"{p}几岁"questions.append(question)return questionsdef create_entity_question():questions = []for _ in range(250):for op in ['大于', '等于', '小于', '是', '有']:profit = np.random.randint(10000, 10000000, 1)[0]question = f"收益{op}{profit}的公司有哪些"questions.append(question)profit = np.random.randint(10000, 10000000, 1)[0]question = f"哪些公司收益{op}{profit}"questions.append(question)for _ in range(250):for op in ['大于', '等于', '小于', '是', '有']:profit = np.random.randint(20, 60, 1)[0]question = f"年龄{op}{profit}的人有哪些"questions.append(question)profit = np.random.randint(20, 60, 1)[0]question = f"哪些人年龄{op}{profit}"questions.append(question)return questionsdef create_relation_question():relation = graph.run('MATCH (n)-[r]->(m) RETURN n.name as name, type(r) as r').to_ndarray()questions = []for r in relation:if str(r[1]) in ['董事', '监事']:question = f"{r[0]}的{r[1]}是谁"questions.append(question)else:question = f"{r[0]}的{r[1]}"questions.append(question)question = f"{r[0]}的{r[1]}是啥"questions.append(question)question = f"{r[0]}的{r[1]}什么"questions.append(question)return questionsq1 = create_entity_question()
q2 = create_attribute_question()
q3 = create_relation_question()df = pd.DataFrame()
df['question'] = q1 + q2 + q3
df['label'] = [0] * len(q1) + [1] * len(q2) + [2] * len(q3)df.to_csv('question_classification.csv', encoding='utf_8_sig', index=False)

data_process

import pandas as pd
import jieba
from collections import defaultdict
import numpy as np
import os__file__ = 'kbqa'
path = os.path.dirname(__file__)def tokenize(text, use_jieba=True):if use_jieba:res = list(jieba.cut(text, cut_all=False))else:res = list(text)return res# 构建词典
def build_vocab(del_word_frequency=0):data = pd.read_csv('question_classification.csv')segment = data['question'].apply(tokenize)word_frequency = defaultdict(int)for row in segment:for i in row:word_frequency[i] += 1word_sort = sorted(word_frequency.items(), key=lambda x: x[1], reverse=True)  # 根据词频降序排序f = open('vocab.txt', 'w', encoding='utf-8')f.write('[PAD]' + "\n" + '[UNK]' + "\n")for d in word_sort:if d[1] > del_word_frequency:f.write(d[0] + "\n")f.close()# 划分训练集和测试集
def split_data(df, split=0.7):df = df.sample(frac=1)length = len(df)train_data = df[0:length - 2000]eval_data = df[length - 2000:]return train_data, eval_datavocab = {}
if os.path.exists(path + '/vocab.txt'):with open(path + '/vocab.txt', encoding='utf-8')as file:for line in file.readlines():vocab[line.strip()] = len(vocab)# 把数据转换成index
def seq2index(seq):seg = tokenize(seq)seg_index = []for s in seg:seg_index.append(vocab.get(s, 1))return seg_index# 统一长度
def padding_seq(X, max_len=10):return np.array([np.concatenate([x, [0] * (max_len - len(x))]) if len(x) < max_len else x[:max_len] for x in X])if __name__ == '__main__':build_vocab(5)

ac_automaton

import ahocorasick
from py2neo import Graphgraph = Graph("http://localhost:7474", auth=("neo4j", "qwer"))
company = graph.run('MATCH (n:company) RETURN n.name as name').to_ndarray()
relation = graph.run('MATCH ()-[r]-() RETURN distinct type(r)').to_ndarray()ac_company = ahocorasick.Automaton()
ac_relation = ahocorasick.Automaton()for key in enumerate(company):ac_company.add_word(key[1][0], key[1][0])
for key in enumerate(relation):ac_relation.add_word(key[1][0], key[1][0])ac_company.make_automaton()
ac_relation.make_automaton()# haystack = '浙江东阳东欣房地产开发有限公司的客户的供应商'
haystack = '衡水中南锦衡房地产有限公司的债券类型'
# haystack = '临沂金丰公社农业服务有限公司的分红方式'
print('question:', haystack)subject = ''
predicate = []for end_index, original_value in ac_company.iter(haystack):start_index = end_index - len(original_value) + 1print('公司实体：', (start_index, end_index, original_value))assert haystack[start_index:start_index + len(original_value)] == original_valuesubject = original_valuefor end_index, original_value in ac_relation.iter(haystack):start_index = end_index - len(original_value) + 1print('关系：', (start_index, end_index, original_value))assert haystack[start_index:start_index + len(original_value)] == original_valuepredicate.append(original_value)for p in predicate:cypher = f'''match (s:company)-[p:`{p}`]-(o) where s.name='{subject}' return o.name'''print(cypher)res = graph.run(cypher).to_ndarray()# print(res)subject = res[0][0]
print('answer:', res[0][0])

torch_utils

import torch
import time
import numpy as np
import sixclass TrainHandler:def __init__(self,train_loader,valid_loader,model,criterion,optimizer,model_path,batch_size=32,epochs=5,scheduler=None,gpu_num=0):self.train_loader = train_loaderself.valid_loader = valid_loaderself.criterion = criterionself.optimizer = optimizerself.model_path = model_pathself.batch_size = batch_sizeself.epochs = epochsself.scheduler = schedulerif torch.cuda.is_available():self.device = torch.device(f'cuda:{gpu_num}')print('Training device is gpu:{gpu_num}')else:self.device = torch.device('cpu')print('Training device is cpu')self.model = model.to(self.device)def _train_func(self):train_loss = 0train_correct = 0for i, (x, y) in enumerate(self.train_loader):self.optimizer.zero_grad()x, y = x.to(self.device).long(), y.to(self.device)output = self.model(x)loss = self.criterion(output, y)train_loss += loss.item()loss.backward()self.optimizer.step()train_correct += (output.argmax(1) == y).sum().item()if self.scheduler is not None:self.scheduler.step()return train_loss / len(self.train_loader), train_correct / len(self.train_loader.dataset)def _test_func(self):valid_loss = 0valid_correct = 0for x, y in self.valid_loader:x, y = x.to(self.device).long(), y.to(self.device)with torch.no_grad():output = self.model(x)loss = self.criterion(output, y)valid_loss += loss.item()valid_correct += (output.argmax(1) == y).sum().item()return valid_loss / len(self.valid_loader), valid_correct / len(self.valid_loader.dataset)def train(self):min_valid_loss = float('inf')for epoch in range(self.epochs):start_time = time.time()train_loss, train_acc = self._train_func()valid_loss, valid_acc = self._test_func()if min_valid_loss > valid_loss:min_valid_loss = valid_losstorch.save(self.model, self.model_path)print(f'\tSave model done valid loss: {valid_loss:.4f}')secs = int(time.time() - start_time)mins = secs / 60secs = secs % 60print('Epoch: %d' % (epoch + 1), " | time in %d minutes, %d seconds" % (mins, secs))print(f'\tLoss: {train_loss:.4f}(train)\t|\tAcc: {train_acc * 100:.1f}%(train)')print(f'\tLoss: {valid_loss:.4f}(valid)\t|\tAcc: {valid_acc * 100:.1f}%(valid)')def torch_text_process():from torchtext import datadef tokenizer(text):import jiebareturn list(jieba.cut(text))TEXT = data.Field(sequential=True, tokenize=tokenizer, lower=True, fix_length=20)LABEL = data.Field(sequential=False, use_vocab=False)all_dataset = data.TabularDataset.splits(path='',train='LCQMC.csv',format='csv',fields=[('sentence1', TEXT), ('sentence2', TEXT), ('label', LABEL)])[0]TEXT.build_vocab(all_dataset)train, valid = all_dataset.split(0.1)(train_iter, valid_iter) = data.BucketIterator.splits(datasets=(train, valid),batch_sizes=(64, 128),sort_key=lambda x: len(x.sentence1))return train_iter, valid_iterdef pad_sequences(sequences, maxlen=None, dtype='int32',padding='post', truncating='pre', value=0.):"""Pads sequences to the same length.This function transforms a list of`num_samples` sequences (lists of integers)into a 2D Numpy array of shape `(num_samples, num_timesteps)`.`num_timesteps` is either the `maxlen` argument if provided,or the length of the longest sequence otherwise.Sequences that are shorter than `num_timesteps`are padded with `value` at the end.Sequences longer than `num_timesteps` are truncatedso that they fit the desired length.The position where padding or truncation happens is determined bythe arguments `padding` and `truncating`, respectively.Pre-padding is the default.# Argumentssequences: List of lists, where each element is a sequence.maxlen: Int, maximum length of all sequences.dtype: Type of the output sequences.To pad sequences with variable length strings, you can use `object`.padding: String, 'pre' or 'post':pad either before or after each sequence.truncating: String, 'pre' or 'post':remove values from sequences larger than`maxlen`, either at the beginning or at the end of the sequences.value: Float or String, padding value.# Returnsx: Numpy array with shape `(len(sequences), maxlen)`# RaisesValueError: In case of invalid values for `truncating` or `padding`,or in case of invalid shape for a `sequences` entry."""if not hasattr(sequences, '__len__'):raise ValueError('`sequences` must be iterable.')num_samples = len(sequences)lengths = []for x in sequences:try:lengths.append(len(x))except TypeError:raise ValueError('`sequences` must be a list of iterables. ''Found non-iterable: ' + str(x))if maxlen is None:maxlen = np.max(lengths)# take the sample shape from the first non empty sequence# checking for consistency in the main loop below.sample_shape = tuple()for s in sequences:if len(s) > 0:sample_shape = np.asarray(s).shape[1:]breakis_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype(dtype, np.unicode_)if isinstance(value, six.string_types) and dtype != object and not is_dtype_str:raise ValueError("`dtype` {} is not compatible with `value`'s type: {}\n""You should set `dtype=object` for variable length strings.".format(dtype, type(value)))x = np.full((num_samples, maxlen) + sample_shape, value, dtype=dtype)for idx, s in enumerate(sequences):if not len(s):continue  # empty list/array was foundif truncating == 'pre':trunc = s[-maxlen:]elif truncating == 'post':trunc = s[:maxlen]else:raise ValueError('Truncating type "%s" ''not understood' % truncating)# check `trunc` has expected shapetrunc = np.asarray(trunc, dtype=dtype)if trunc.shape[1:] != sample_shape:raise ValueError('Shape of sample %s of sequence at position %s ''is different from expected shape %s' %(trunc.shape[1:], idx, sample_shape))if padding == 'post':x[idx, :len(trunc)] = truncelif padding == 'pre':x[idx, -len(trunc):] = truncelse:raise ValueError('Padding type "%s" not understood' % padding)return xif __name__ == '__main__':torch_text_process()

text_cnn

import torch
from torch import nnclass TextCNN(nn.Module):def __init__(self, vocab_len, embedding_size, n_class):super().__init__()self.embedding = nn.Embedding(vocab_len, embedding_size)self.cnn1 = nn.Conv2d(in_channels=1, out_channels=100, kernel_size=[3, embedding_size])self.cnn2 = nn.Conv2d(in_channels=1, out_channels=100, kernel_size=[4, embedding_size])self.cnn3 = nn.Conv2d(in_channels=1, out_channels=100, kernel_size=[5, embedding_size])self.max_pool1 = nn.MaxPool1d(kernel_size=8)self.max_pool2 = nn.MaxPool1d(kernel_size=7)self.max_pool3 = nn.MaxPool1d(kernel_size=6)self.drop_out = nn.Dropout(0.2)self.full_connect = nn.Linear(300, n_class)def forward(self, x):embedding = self.embedding(x)embedding = embedding.unsqueeze(1)cnn1_out = self.cnn1(embedding).squeeze(-1)cnn2_out = self.cnn2(embedding).squeeze(-1)cnn3_out = self.cnn3(embedding).squeeze(-1)out1 = self.max_pool1(cnn1_out)out2 = self.max_pool2(cnn2_out)out3 = self.max_pool3(cnn3_out)out = torch.cat([out1, out2, out3], dim=1).squeeze(-1)out = self.drop_out(out)out = self.full_connect(out)# out = torch.softmax(out, dim=-1).squeeze(dim=-1)return out

train

import torch
from torch.utils.data import TensorDataset, DataLoader
from kbqa.torch_utils import TrainHandler
from kbqa.data_process import *
from kbqa.text_cnn import TextCNN# df = pd.read_csv('question_classification.csv')
# print(df['label'].value_counts())def load_data(batch_size=32):df = pd.read_csv('kbqa/question_classification.csv')train_df, eval_df = split_data(df)train_x = df['question']train_y = df['label']valid_x = eval_df['question']valid_y = eval_df['label']train_x = padding_seq(train_x.apply(seq2index))train_y = np.array(train_y)valid_x = padding_seq(valid_x.apply(seq2index))valid_y = np.array(valid_y)train_data_set = TensorDataset(torch.from_numpy(train_x),torch.from_numpy(train_y))valid_data_set = TensorDataset(torch.from_numpy(valid_x),torch.from_numpy(valid_y))train_data_loader = DataLoader(dataset=train_data_set, batch_size=batch_size, shuffle=True)valid_data_loader = DataLoader(dataset=valid_data_set, batch_size=batch_size, shuffle=True)return train_data_loader, valid_data_loadertrain_loader, valid_loader = load_data(batch_size=64)model = TextCNN(1289, 256, 3)# 原model = TextCNN(1141, 256, 3)，1289根据vocat.txt行数
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.9)
model_path = 'text_cnn.p'
handler = TrainHandler(train_loader,valid_loader,model,criterion,optimizer,model_path,batch_size=32,epochs=5,scheduler=None,gpu_num=0)
handler.train()

main

import torch
from kbqa.data_process import *
import ahocorasick
from py2neo import Graph
import re
import tracebackmodel = torch.load('kbqa/text_cnn.p', map_location=torch.device('cpu'))
model.eval()graph = Graph("http://localhost:7474", auth=("neo4j", "qwer"))
company = graph.run('MATCH (n:company) RETURN n.name as name').to_ndarray()
person = graph.run('MATCH (n:person) RETURN n.name as name').to_ndarray()
relation = graph.run('MATCH ()-[r]-() RETURN distinct type(r)').to_ndarray()ac_company = ahocorasick.Automaton()
ac_person = ahocorasick.Automaton()
ac_relation = ahocorasick.Automaton()for key in enumerate(company):ac_company.add_word(key[1][0], key[1][0])
for key in enumerate(person):ac_person.add_word(key[1][0], key[1][0])
for key in enumerate(relation):ac_relation.add_word(key[1][0], key[1][0])
ac_relation.add_word('年龄', '年龄')
ac_relation.add_word('年纪', '年纪')
ac_relation.add_word('收入', '收入')
ac_relation.add_word('收益', '收益')ac_company.make_automaton()
ac_person.make_automaton()
ac_relation.make_automaton()def classification_predict(s):s = seq2index(s)s = torch.from_numpy(padding_seq([s])).long() #.cuda().long()out = model(s)out = out.cpu().data.numpy()print(out)return out.argmax(1)[0]def entity_link(text):subject = []subject_type = Nonefor end_index, original_value in ac_company.iter(text):start_index = end_index - len(original_value) + 1print('实体：', (start_index, end_index, original_value))assert text[start_index:start_index + len(original_value)] == original_valuesubject.append(original_value)subject_type = 'company'for end_index, original_value in ac_person.iter(text):start_index = end_index - len(original_value) + 1print('实体：', (start_index, end_index, original_value))assert text[start_index:start_index + len(original_value)] == original_valuesubject.append(original_value)subject_type = 'person'return subject[0], subject_typedef get_op(text):pattern = re.compile(r'\d+')num = pattern.findall(text)op = Noneif '大于' in text:op = '>'elif '小于' in text:op = '<'elif '等于' in text or '是' in text:op = '='return op, float(num[0])def kbqa(text):print('*' * 100)cls = classification_predict(text)print('question type:', cls)res = ''if cls == 0:op, num = get_op(text)subject_type = ''attribute = ''for w in ['年龄', '年纪']:if w in text:subject_type = 'person'attribute = 'age'breakfor w in ['收入', '收益']:if w in text:subject_type = 'company'attribute = 'profit'breakcypher = f'match (n:{subject_type}) where n.{attribute}{op}{num} return n.name'print(cypher)res = graph.run(cypher).to_ndarray()elif cls == 1:# 查询属性subject, subject_type = entity_link(text)predicate = ''for w in ['年龄', '年纪']:if w in text and subject_type == 'person':predicate = 'age'breakfor w in ['收入', '收益']:if w in text and subject_type == 'company':predicate = 'profit'breakcypher = f'''match (n:{subject_type}) where n.name='{subject}' return n.{predicate}'''print(cypher)res = graph.run(cypher).to_ndarray()elif cls == 2:subject = ''for end_index, original_value in ac_company.iter(text):start_index = end_index - len(original_value) + 1print('公司实体：', (start_index, end_index, original_value))assert text[start_index:start_index + len(original_value)] == original_valuesubject = original_valuepredicate = []for end_index, original_value in ac_relation.iter(text):start_index = end_index - len(original_value) + 1print('关系：', (start_index, end_index, original_value))assert text[start_index:start_index + len(original_value)] == original_valuepredicate.append(original_value)for i, p in enumerate(predicate):cypher = f'''match (s:company)-[p:`{p}`]->(o) where s.name='{subject}' return o.name'''print(cypher)res = graph.run(cypher).to_ndarray()subject = res[0][0]if i == len(predicate) - 1:breaknew_index = text.index(p) + len(p)new_question = subject + str(text[new_index:])print('new question:', new_question)res = kbqa(new_question)breakreturn resif __name__ == '__main__':while 1:try:text = input('text:')res = kbqa(text)print(res)except:print(traceback.format_exc())

图谱问答实战小结

模型的整体结构：ac自动机+找实体+多跳问答

ps：这里实体没有多个，用不到实体消歧，这里我们使用的是匹配的方法

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学习的参考资料：
七月在线NLP高级班

代码参考：
https://github.com/terrifyzhao/neo4j_graph