KBQA Knowledge Graph Question Answering
- 1. Learning graph database Neo4j
- 2. Q&A on Medical Knowledge Graph
-
- 2.1 Build the python virtual environment required by the project.
- 2.2 Run data_spider.py to crawl webpage data and save it to mongodb.
- 2.3 Run the build_medicalgraph.py file, import the json file into the neo4j database and extract the feature words.
- 2.4 Run the chatbot_graph.py file for human-computer interaction.
- references
1. Learning graph database Neo4j
1.1 Introduction
Knowledge map data includes entities, attributes, relationships, etc., and is generally stored in graph databases (Graph Databases), and Neo4j is the most common graph database. Download Neo4j for the corresponding system from the official website .
cypher: Neo4J's declarative graph query language, which allows users to efficiently query graph data without writing graph structure traversal code.
1.2 Create a node
1. Open the browser of a library in neo4j and delete the data in the library
MATCH (n) DETACH DELETE n
MATCH is a matching operation, and the parentheses () represent a node node (which can be understood as the brackets are similar to a circle), and the n in the brackets is an identifier.
2. Create 6 character nodes
CREATE (n:Person {
name:'John'}) RETURN n;
CREATE (n:Person {
name:'Sally'}) RETURN n;
CREATE (n:Person {
name:'Steve'}) RETURN n;
CREATE (n:Person {
name:'Mike'}) RETURN n;
CREATE (n:Person {
name:'Liz'}) RETURN n;
CREATE (n:Person {
name:'Shawn'}) RETURN n
CREATE is a creation operation, Person is a label, representing the type of node; curly braces {} represent the attribute of the node, which is similar to a Python dictionary; the meaning of this statement is to create a node with the label Person, which has a name attribute, The property value is John.
3. Create 5 regional nodes
CREATE (n:Location {
city:'Miami', state:'FL'});
CREATE (n:Location {
city:'Boston', state:'MA'});
CREATE (n:Location {
city:'Lynn', state:'MA'});
CREATE (n:Location {
city:'Portland', state:'ME'});
CREATE (n:Location {
city:'San Francisco', state:'CA'})
The node type is Location, and the attributes include city and state.
1.3 Create relationship
1. Create a relationship of the same type
MATCH (a:Person{
name:'Liz'}),(b:Person{
name:'Mike'}) MERGE (a)-[:FRIENDS]->(b);
MATCH (a:Person {
name:'Shawn'}), (b:Person {
name:'John'}) MERGE (a)-[:FRIENDS {
since:2012}]->(b);
MATCH (a:Person {
name:'Mike'}), (b:Person {
name:'Shawn'}) MERGE (a)-[:FRIENDS {
since:2006}]->(b);
MATCH (a:Person {
name:'Sally'}), (b:Person {
name:'Steve'}) MERGE (a)-[:FRIENDS {
since:2006}]->(b);
MATCH (a:Person {
name:'Liz'}), (b:Person {
name:'John'}) MERGE (a)-[:MARRIED {
since:1998}]->(b);
MATCH (a:Person {
name:'Shawn'}), (b:Person {
name:'Sally'}) MERGE (a)-[:FRIENDS since:2001}]->(b)
The square brackets [] are the relationship, and FRIENDS is the type of relationship; note that the arrow –> is directional, indicating the relationship from a to b. In this way, the FRIENDS relationship is established between Liz and Mike; {} in the relationship [] is to add attributes to the relationship.
2. Different types of relationship creation
MATCH (a:Person {
name:'John'}), (b:Location {
city:'Boston'}) MERGE (a)-[:BORN_IN {
year:1978}]->(b);
MATCH (a:Person {
name:'Liz'}), (b:Location {
city:'Boston'}) MERGE (a)-[:BORN_IN {
year:1981}]->(b);
MATCH (a:Person {
name:'Mike'}), (b:Location {
city:'San Francisco'}) MERGE (a)-[:BORN_IN {
year:1960}]->(b);
MATCH (a:Person {
name:'Shawn'}), (b:Location {
city:'Miami'}) MERGE (a)-[:BORN_IN {
year:1960}]->(b);
MATCH (a:Person {
name:'Steve'}), (b:Location {
city:'Lynn'}) MERGE (a)-[:BORN_IN {
year:1970}]->(b)
3. Create a relationship when you create a node
CREATE (a:Person {
name:'Todd'})-[r:FRIENDS]->(b:Person {
name:'Carlos'})
1.4 Query
1. Query all the people born in Boston
MATCH (a:Person)-[:BORN_IN]->(b:Location {
city:'Boston'}) RETURN a,b
2. Query all related nodes
MATCH (a)--() RETURN a
3. Query all nodes that have external relationships (there are arrows here, indicating that the direction of the relationship is outward)
MATCH (a)-->() RETURN a
4. Query all nodes that have external relationships, as well as the relationship type
MATCH (a)-[r]->() RETURN a.name, type(r)
5. Query all nodes with marriage relationship
MATCH (n)-[:MARRIED]-() RETURN n
6. Find someone's friends of friends
MATCH (a:Person {
name:'Mike'})-[r1:FRIENDS]-()-[r2:FRIENDS]-(friend_of_a_friend) RETURN friend_of_a_friend.name AS fofName
1.5 Modification and deletion
1. Add/modify node attributes
MATCH (a:Person {
name:'Liz'}) SET a.age=34;
MATCH (a:Person {
name:'Shawn'}) SET a.age=32;
MATCH (a:Person {
name:'John'}) SET a.age=44;
MATCH (a:Person {
name:'Mike'}) SET a.age=25
SET means modify operation
2. Delete the attribute of the node
MATCH (a:Person {
name:'Liz'}) REMOVE a.age
REMOVE means delete node attribute operation
3. Delete node
MATCH (a:Location {
city:'Portland'}) DELETE a
DELETE is to delete the node operation
4. Delete related nodes
MATCH (a:Person {
name:'Todd'})-[rel]-(b:Person) DELETE a,b,rel
2. Q&A on Medical Knowledge Graph
Here I use the automatic question and answer system of medical knowledge graph in the github project of Mr. Liu Huanyong to learn.
2.1 Build the python virtual environment required by the project.
conda create -n kbqa python
conda activate kbqa
pip install lxml
pip install pymongdb
pip install py2neo
pip install pyahocorasick -i HTTPS://mirrors.aliyun.com/pypi/simple/
Install mongdb , refer to blog post .
Install neo4j , refer to blog post . The versions of neo4j and java must correspond, otherwise an error will appear when running the neo4j console Run with '–verbose' for a more detailed error message, here you can refer to blog posts and blog posts . I have been working on this for a long time. In fact, there are two problems. Add the bin directory of java and the bin directory of neo4j to the path in the environment variable; in this way, I can view the versions of these two in cmd, and then it appears when I run the neo4j consolePlease use Oracle® Java™ 17, OpenJDK™ 17 to run Neo4j, the meaning of this sentence is that java needs to use version 17, not other versions. Here, pay attention to which version you need. Version 17.
There is always an error == main ERROR Cannot access RandomAccessFile java.io.FileNotFoundException: D:\Softwares\Neo4j\neo4j-community-5.2.0\logs\neo4j.log (Access is denied.) == This denial of access is permission Not enough, I put all the files in the program files folder of the c drive.
StatusLogger Unable to create file D:\Program Files\neo4j-community-4.4.0\logs\debug.log, indicating that the path has no permission, modify the folder permission to read and write.
2.2 Run data_spider.py to crawl webpage data and save it to mongodb.
data_spider.py
#!/usr/bin/env python3
# coding: utf-8
import urllib.request
import urllib.parse
from lxml import etree
import pymongo
import re
class CrimeSpider:
def __init__(self):
self.conn = pymongo.MongoClient()
self.db = self.conn['medical']#库
self.col = self.db['data']#表
'''根据url，请求html'''
def get_html(self, url):
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/51.0.2704.63 Safari/537.36'}
req = urllib.request.Request(url=url, headers=headers)
res = urllib.request.urlopen(req)
html = res.read().decode('gbk')
return html
'''url解析'''
def url_parser(self, content):
selector = etree.HTML(content)
urls = ['http://www.anliguan.com' + i for i in selector.xpath('//h2[@class="item-title"]/a/@href')]
return urls
'''测试'''
def spider_main(self):
for page in range(1, 100):
try:
basic_url = 'http://jib.xywy.com/il_sii/gaishu/%s.htm'%page
cause_url = 'http://jib.xywy.com/il_sii/cause/%s.htm'%page
prevent_url = 'http://jib.xywy.com/il_sii/prevent/%s.htm'%page
symptom_url = 'http://jib.xywy.com/il_sii/symptom/%s.htm'%page
inspect_url = 'http://jib.xywy.com/il_sii/inspect/%s.htm'%page
treat_url = 'http://jib.xywy.com/il_sii/treat/%s.htm'%page
food_url = 'http://jib.xywy.com/il_sii/food/%s.htm'%page
drug_url = 'http://jib.xywy.com/il_sii/drug/%s.htm'%page
data = {
}
data['url'] = basic_url
data['basic_info'] = self.basicinfo_spider(basic_url)
data['cause_info'] = self.common_spider(cause_url)
data['prevent_info'] = self.common_spider(prevent_url)
data['symptom_info'] = self.symptom_spider(symptom_url)
data['inspect_info'] = self.inspect_spider(inspect_url)
data['treat_info'] = self.treat_spider(treat_url)
data['food_info'] = self.food_spider(food_url)
data['drug_info'] = self.drug_spider(drug_url)
print(page, basic_url)
self.col.insert_one(data)
except Exception as e:
print(e, page)
return
'''基本信息解析'''
def basicinfo_spider(self, url):
html = self.get_html(url)
selector = etree.HTML(html)
title = selector.xpath('//title/text()')[0]
category = selector.xpath('//div[@class="wrap mt10 nav-bar"]/a/text()')
desc = selector.xpath('//div[@class="jib-articl-con jib-lh-articl"]/p/text()')
ps = selector.xpath('//div[@class="mt20 articl-know"]/p')
infobox = []
for p in ps:
info = p.xpath('string(.)').replace('\r','').replace('\n','').replace('\xa0', '').replace(' ', '').replace('\t','')
infobox.append(info)
basic_data = {
}
basic_data['category'] = category
basic_data['name'] = title.split('的简介')[0]
basic_data['desc'] = desc
basic_data['attributes'] = infobox
return basic_data
'''treat_infobox治疗解析'''
def treat_spider(self, url):
html = self.get_html(url)
selector = etree.HTML(html)
ps = selector.xpath('//div[starts-with(@class,"mt20 articl-know")]/p')
infobox = []
for p in ps:
info = p.xpath('string(.)').replace('\r','').replace('\n','').replace('\xa0', '').replace(' ', '').replace('\t','')
infobox.append(info)
return infobox
'''treat_infobox治疗解析'''
def drug_spider(self, url):
html = self.get_html(url)
selector = etree.HTML(html)
drugs = [i.replace('\n','').replace('\t', '').replace(' ','') for i in selector.xpath('//div[@class="fl drug-pic-rec mr30"]/p/a/text()')]
return drugs
'''food治疗解析'''
def food_spider(self, url):
html = self.get_html(url)
selector = etree.HTML(html)
divs = selector.xpath('//div[@class="diet-img clearfix mt20"]')
try:
food_data = {
}
food_data['good'] = divs[0].xpath('./div/p/text()')
food_data['bad'] = divs[1].xpath('./div/p/text()')
food_data['recommand'] = divs[2].xpath('./div/p/text()')
except:
return {
}
return food_data
'''症状信息解析'''
def symptom_spider(self, url):
html = self.get_html(url)
selector = etree.HTML(html)
symptoms = selector.xpath('//a[@class="gre" ]/text()')
ps = selector.xpath('//p')
detail = []
for p in ps:
info = p.xpath('string(.)').replace('\r','').replace('\n','').replace('\xa0', '').replace(' ', '').replace('\t','')
detail.append(info)
symptoms_data = {
}
symptoms_data['symptoms'] = symptoms
symptoms_data['symptoms_detail'] = detail
return symptoms, detail
'''检查信息解析'''
def inspect_spider(self, url):
html = self.get_html(url)
selector = etree.HTML(html)
inspects = selector.xpath('//li[@class="check-item"]/a/@href')
return inspects
'''通用解析模块'''
def common_spider(self, url):
html = self.get_html(url)
selector = etree.HTML(html)
ps = selector.xpath('//p')
infobox = []
for p in ps:
info = p.xpath('string(.)').replace('\r', '').replace('\n', '').replace('\xa0', '').replace(' ','').replace('\t', '')
if info:
infobox.append(info)
return '\n'.join(infobox)
'''检查项抓取模块'''
def inspect_crawl(self):
for page in range(1, 36):
try:
url = 'http://jck.xywy.com/jc_%s.html'%page#这里是要爬取的网站链接
html = self.get_html(url)
data = {
}
data['url']= url
data['html'] = html
self.db['jc'].insert_one(data)
print(url)
except Exception as e:
print(e)
handler = CrimeSpider()
#抓取网页数据并保存到本地的mongodb数据库。库名叫medical：self.db = self.conn['medical']
handler.spider_main()#self.db['data'].insert_one('url'，'basic_info'，'cause_info'，'prevent_info'
#'symptom_info'，'inspect_info'，'treat_info'，'food_info'，'drug_info')中插入数据，
handler.inspect_crawl()#self.db['jc'].insert_one('url'，'html')中插入数据
report error：TypeError: ‘Collection’ object is not callable. If you meant to call the ‘insert’ method on a ‘Collection’ object it is failing because no such method exists。
reason: Collection.insert method is removed in PyMongo 4.0.*, use Collection.insert_one or Collection.insert_many instead.
solve: Modify the insertion method in the data_spider.py file.
self.col.insert_one(data)
self.db['jc'].insert_one(data)
report error:localhost:27017: [WinError 10061] Could not connect because the target computer actively refused it.
reason: Because the mongodb database is linked in the program, but I have not opened the mongodb background.
solve: Start mongodb in the background, open cdm in the bin directory of mongodb
mongod.exe --dbpath D:\mongodb
Export the data into a json file in the mongodb database.
2.3 Run the build_medicalgraph.py file, import the json file into the neo4j database and extract the feature words.
#!/usr/bin/env python3
# coding: utf-8
import os
import json
from py2neo import Graph,Node
class MedicalGraph:
def __init__(self):
cur_dir = '/'.join(os.path.abspath(__file__).split('/')[:-1])
self.data_path = os.path.join(cur_dir, 'data/medical.json')
# self.g = Graph(
# host="127.0.0.1", # neo4j 搭载服务器的ip地址，ifconfig可获取到
# http_port=7474, # neo4j 服务器监听的端口号
# user="neo4j", # 数据库user name，如果没有更改过，应该是neo4j
# password="neo4j")
self.g = Graph('http://localhost:7474/', auth=('neo4j', '123qwe'))
'''读取文件'''
def read_nodes(self):
# 共７类节点(7+1)
drugs = [] # 药品
foods = [] # 食物
checks = [] # 检查
departments = [] #科室
producers = [] #药品大类
diseases = [] #疾病
symptoms = []#症状
disease_infos = []#疾病信息
# 构建节点实体关系(11)
rels_department = [] # 科室－科室关系
rels_noteat = [] # 疾病－忌吃食物关系
rels_doeat = [] # 疾病－宜吃食物关系
rels_recommandeat = [] # 疾病－推荐吃食物关系
rels_commonddrug = [] # 疾病－通用药品关系
rels_recommanddrug = [] # 疾病－热门药品关系
rels_check = [] # 疾病－检查关系
rels_drug_producer = [] # 厂商－药物关系
rels_symptom = [] #疾病症状关系
rels_acompany = [] # 疾病并发关系
rels_category = [] # 疾病与科室之间的关系
count = 0
for data in open(self.data_path,encoding='utf-8'):
disease_dict = {
}
count += 1
print(count)
data_json = json.loads(data)
disease = data_json['name']
disease_dict['name'] = disease
diseases.append(disease)
disease_dict['desc'] = ''
disease_dict['prevent'] = ''
disease_dict['cause'] = ''
disease_dict['easy_get'] = ''
disease_dict['cure_department'] = ''
disease_dict['cure_way'] = ''
disease_dict['cure_lasttime'] = ''
disease_dict['symptom'] = ''
disease_dict['cured_prob'] = ''
if 'symptom' in data_json:
symptoms += data_json['symptom']
for symptom in data_json['symptom']:
rels_symptom.append([disease, symptom])
if 'acompany' in data_json:
for acompany in data_json['acompany']:
rels_acompany.append([disease, acompany])
if 'desc' in data_json:
disease_dict['desc'] = data_json['desc']
if 'prevent' in data_json:
disease_dict['prevent'] = data_json['prevent']
if 'cause' in data_json:
disease_dict['cause'] = data_json['cause']
if 'get_prob' in data_json:
disease_dict['get_prob'] = data_json['get_prob']
if 'easy_get' in data_json:
disease_dict['easy_get'] = data_json['easy_get']
if 'cure_department' in data_json:
cure_department = data_json['cure_department']
if len(cure_department) == 1:
rels_category.append([disease, cure_department[0]])
if len(cure_department) == 2:
big = cure_department[0]
small = cure_department[1]
rels_department.append([small, big])
rels_category.append([disease, small])
disease_dict['cure_department'] = cure_department
departments += cure_department
if 'cure_way' in data_json:
disease_dict['cure_way'] = data_json['cure_way']
if 'cure_lasttime' in data_json:
disease_dict['cure_lasttime'] = data_json['cure_lasttime']
if 'cured_prob' in data_json:
disease_dict['cured_prob'] = data_json['cured_prob']
if 'common_drug' in data_json:
common_drug = data_json['common_drug']
for drug in common_drug:
rels_commonddrug.append([disease, drug])
drugs += common_drug
if 'recommand_drug' in data_json:
recommand_drug = data_json['recommand_drug']
drugs += recommand_drug
for drug in recommand_drug:
rels_recommanddrug.append([disease, drug])
if 'not_eat' in data_json:
not_eat = data_json['not_eat']
for _not in not_eat:
rels_noteat.append([disease, _not])
foods += not_eat
do_eat = data_json['do_eat']
for _do in do_eat:
rels_doeat.append([disease, _do])
foods += do_eat
recommand_eat = data_json['recommand_eat']
for _recommand in recommand_eat:
rels_recommandeat.append([disease, _recommand])
foods += recommand_eat
if 'check' in data_json:
check = data_json['check']
for _check in check:
rels_check.append([disease, _check])
checks += check
if 'drug_detail' in data_json:
drug_detail = data_json['drug_detail']
producer = [i.split('(')[0] for i in drug_detail]
rels_drug_producer += [[i.split('(')[0], i.split('(')[-1].replace(')', '')] for i in drug_detail]
producers += producer
disease_infos.append(disease_dict)
return set(drugs), set(foods), set(checks), set(departments), set(producers), set(symptoms), set(diseases), disease_infos,\
rels_check, rels_recommandeat, rels_noteat, rels_doeat, rels_department, rels_commonddrug, rels_drug_producer, rels_recommanddrug,\
rels_symptom, rels_acompany, rels_category
'''建立节点'''
def create_node(self, label, nodes):
count = 0
for node_name in nodes:
node = Node(label, name=node_name)
self.g.create(node)
count += 1
print(count, len(nodes))
return
'''创建知识图谱中心疾病的节点'''
def create_diseases_nodes(self, disease_infos):
count = 0
for disease_dict in disease_infos:
node = Node("Disease", name=disease_dict['name'], desc=disease_dict['desc'],
prevent=disease_dict['prevent'] ,cause=disease_dict['cause'],
easy_get=disease_dict['easy_get'],cure_lasttime=disease_dict['cure_lasttime'],
cure_department=disease_dict['cure_department']
,cure_way=disease_dict['cure_way'] , cured_prob=disease_dict['cured_prob'])
self.g.create(node)
count += 1
print(count)
return
'''创建知识图谱实体节点类型schema'''
def create_graphnodes(self):
Drugs, Foods, Checks, Departments, Producers, Symptoms, Diseases, disease_infos,rels_check, rels_recommandeat, rels_noteat, rels_doeat, rels_department, rels_commonddrug, rels_drug_producer, rels_recommanddrug,rels_symptom, rels_acompany, rels_category = self.read_nodes()
self.create_diseases_nodes(disease_infos)
self.create_node('Drug', Drugs)
print(len(Drugs))
self.create_node('Food', Foods)
print(len(Foods))
self.create_node('Check', Checks)
print(len(Checks))
self.create_node('Department', Departments)
print(len(Departments))
self.create_node('Producer', Producers)
print(len(Producers))
self.create_node('Symptom', Symptoms)
return
'''创建实体关系边'''
def create_graphrels(self):
Drugs, Foods, Checks, Departments, Producers, Symptoms, Diseases, disease_infos, rels_check, rels_recommandeat, rels_noteat, rels_doeat, rels_department, rels_commonddrug, rels_drug_producer, rels_recommanddrug,rels_symptom, rels_acompany, rels_category = self.read_nodes()
self.create_relationship('Disease', 'Food', rels_recommandeat, 'recommand_eat', '推荐食谱')
self.create_relationship('Disease', 'Food', rels_noteat, 'no_eat', '忌吃')
self.create_relationship('Disease', 'Food', rels_doeat, 'do_eat', '宜吃')
self.create_relationship('Department', 'Department', rels_department, 'belongs_to', '属于')
self.create_relationship('Disease', 'Drug', rels_commonddrug, 'common_drug', '常用药品')
self.create_relationship('Producer', 'Drug', rels_drug_producer, 'drugs_of', '生产药品')
self.create_relationship('Disease', 'Drug', rels_recommanddrug, 'recommand_drug', '好评药品')
self.create_relationship('Disease', 'Check', rels_check, 'need_check', '诊断检查')
self.create_relationship('Disease', 'Symptom', rels_symptom, 'has_symptom', '症状')
self.create_relationship('Disease', 'Disease', rels_acompany, 'acompany_with', '并发症')
self.create_relationship('Disease', 'Department', rels_category, 'belongs_to', '所属科室')
'''创建实体关联边'''
def create_relationship(self, start_node, end_node, edges, rel_type, rel_name):
count = 0
# 去重处理
set_edges = []
for edge in edges:
set_edges.append('###'.join(edge))
all = len(set(set_edges))
for edge in set(set_edges):
edge = edge.split('###')
p = edge[0]
q = edge[1]
query = "match(p:%s),(q:%s) where p.name='%s'and q.name='%s' create (p)-[rel:%s{name:'%s'}]->(q)" % (
start_node, end_node, p, q, rel_type, rel_name)
try:
self.g.run(query)
count += 1
print(rel_type, count, all)
except Exception as e:
print(e)
return
'''导出数据'''
def export_data(self):
Drugs, Foods, Checks, Departments, Producers, Symptoms, Diseases, disease_infos, rels_check, rels_recommandeat, rels_noteat, rels_doeat, rels_department, rels_commonddrug, rels_drug_producer, rels_recommanddrug, rels_symptom, rels_acompany, rels_category = self.read_nodes()
f_drug = open('drug.txt', 'w+')
f_food = open('food.txt', 'w+')
f_check = open('check.txt', 'w+')
f_department = open('department.txt', 'w+')
f_producer = open('producer.txt', 'w+')
f_symptom = open('symptoms.txt', 'w+')
f_disease = open('disease.txt', 'w+')
f_drug.write('\n'.join(list(Drugs)))
f_food.write('\n'.join(list(Foods)))
f_check.write('\n'.join(list(Checks)))
f_department.write('\n'.join(list(Departments)))
f_producer.write('\n'.join(list(Producers)))
f_symptom.write('\n'.join(list(Symptoms)))
f_disease.write('\n'.join(list(Diseases)))
f_drug.close()
f_food.close()
f_check.close()
f_department.close()
f_producer.close()
f_symptom.close()
f_disease.close()
return
if __name__ == '__main__':
handler = MedicalGraph()
print("step1:导入图谱节点中")
handler.create_graphnodes()
print("step2:导入图谱边中")
handler.create_graphrels()
#handler.export_data()#json>txt，抽取特证词
report error：ValueError: The following settings are not supported
reason: Py2neo defaults to the latest version, the version is too high.
solve: New syntax for diagram linking to databases.
self.g = Graph('http://localhost:7474/', auth=("neo4j", "123qwe"))
report error：py2neo.errors.ConnectionUnavailable: (‘Cannot open connection to %r’, ConnectionProfile(‘http://localhost:7474’))
reason: did not start neo4j
solve：
neo4j install-service
neo4j start
Open http://localhost:7474/
report error：‘gbk‘ codec can‘t decode byte 0xaf in position 81: illegal multibyte sequence
reason: The encoding and decoding formats of the file are inconsistent
solve：
for data in open(self.data_path,encoding='utf-8'):
After successfully running the neo4j and build_medicalgraph.py files, you can see the database diagram in html.
2.4 Run the chatbot_graph.py file for human-computer interaction.
Three files are used here: question_classifier.py: classify the questions that can be answered; question_parser.py: analyze the questions raised by users and belong to them; answer_search.py: search for answers in the neo4j library for this question .
2.4.1 question_classifier.py question classification
#!/usr/bin/env python3
# coding: utf-8
import os
import ahocorasick
class QuestionClassifier:
def __init__(self):
cur_dir = '/'.join(os.path.abspath(__file__).split('/')[:-1])
# 特征词路径
self.disease_path = os.path.join(cur_dir, 'dict/disease.txt')
self.department_path = os.path.join(cur_dir, 'dict/department.txt')
self.check_path = os.path.join(cur_dir, 'dict/check.txt')
self.drug_path = os.path.join(cur_dir, 'dict/drug.txt')
self.food_path = os.path.join(cur_dir, 'dict/food.txt')
self.producer_path = os.path.join(cur_dir, 'dict/producer.txt')
self.symptom_path = os.path.join(cur_dir, 'dict/symptom.txt')
self.deny_path = os.path.join(cur_dir, 'dict/deny.txt')
# 加载特征词
self.disease_wds= [i.strip() for i in open(self.disease_path,encoding='utf-8') if i.strip()]
self.department_wds= [i.strip() for i in open(self.department_path,encoding='utf-8') if i.strip()]
self.check_wds= [i.strip() for i in open(self.check_path,encoding='utf-8') if i.strip()]
self.drug_wds= [i.strip() for i in open(self.drug_path,encoding='utf-8') if i.strip()]
self.food_wds= [i.strip() for i in open(self.food_path,encoding='utf-8') if i.strip()]
self.producer_wds= [i.strip() for i in open(self.producer_path,encoding='utf-8') if i.strip()]
self.symptom_wds= [i.strip() for i in open(self.symptom_path,encoding='utf-8') if i.strip()]
self.region_words = set(self.department_wds + self.disease_wds + self.check_wds + self.drug_wds + self.food_wds + self.producer_wds + self.symptom_wds)
self.deny_words = [i.strip() for i in open(self.deny_path,encoding='utf-8') if i.strip()]
# 构造领域actree
self.region_tree = self.build_actree(list(self.region_words))
# 构建词典
self.wdtype_dict = self.build_wdtype_dict()
# 问句疑问词
self.symptom_qwds = ['症状', '表征', '现象', '症候', '表现']
self.cause_qwds = ['原因','成因', '为什么', '怎么会', '怎样才', '咋样才', '怎样会', '如何会', '为啥', '为何', '如何才会', '怎么才会', '会导致', '会造成']
self.acompany_qwds = ['并发症', '并发', '一起发生', '一并发生', '一起出现', '一并出现', '一同发生', '一同出现', '伴随发生', '伴随', '共现']
self.food_qwds = ['饮食', '饮用', '吃', '食', '伙食', '膳食', '喝', '菜' ,'忌口', '补品', '保健品', '食谱', '菜谱', '食用', '食物','补品']
self.drug_qwds = ['药', '药品', '用药', '胶囊', '口服液', '炎片']
self.prevent_qwds = ['预防', '防范', '抵制', '抵御', '防止','躲避','逃避','避开','免得','逃开','避开','避掉','躲开','躲掉','绕开',
'怎样才能不', '怎么才能不', '咋样才能不','咋才能不', '如何才能不',
'怎样才不', '怎么才不', '咋样才不','咋才不', '如何才不',
'怎样才可以不', '怎么才可以不', '咋样才可以不', '咋才可以不', '如何可以不',
'怎样才可不', '怎么才可不', '咋样才可不', '咋才可不', '如何可不']
self.lasttime_qwds = ['周期', '多久', '多长时间', '多少时间', '几天', '几年', '多少天', '多少小时', '几个小时', '多少年']
self.cureway_qwds = ['怎么治疗', '如何医治', '怎么医治', '怎么治', '怎么医', '如何治', '医治方式', '疗法', '咋治', '怎么办', '咋办', '咋治']
self.cureprob_qwds = ['多大概率能治好', '多大几率能治好', '治好希望大么', '几率', '几成', '比例', '可能性', '能治', '可治', '可以治', '可以医']
self.easyget_qwds = ['易感人群', '容易感染', '易发人群', '什么人', '哪些人', '感染', '染上', '得上']
self.check_qwds = ['检查', '检查项目', '查出', '检查', '测出', '试出']
self.belong_qwds = ['属于什么科', '属于', '什么科', '科室']
self.cure_qwds = ['治疗什么', '治啥', '治疗啥', '医治啥', '治愈啥', '主治啥', '主治什么', '有什么用', '有何用', '用处', '用途',
'有什么好处', '有什么益处', '有何益处', '用来', '用来做啥', '用来作甚', '需要', '要']
print('model init finished ......')
return
'''分类主函数'''
def classify(self, question):
data = {
}
medical_dict = self.check_medical(question)
if not medical_dict:
return {
}
data['args'] = medical_dict
#收集问句当中所涉及到的实体类型
types = []
for type_ in medical_dict.values():
types += type_
question_type = 'others'
question_types = []
# 症状
if self.check_words(self.symptom_qwds, question) and ('disease' in types):
question_type = 'disease_symptom'
question_types.append(question_type)
if self.check_words(self.symptom_qwds, question) and ('symptom' in types):
question_type = 'symptom_disease'
question_types.append(question_type)
# 原因
if self.check_words(self.cause_qwds, question) and ('disease' in types):
question_type = 'disease_cause'
question_types.append(question_type)
# 并发症
if self.check_words(self.acompany_qwds, question) and ('disease' in types):
question_type = 'disease_acompany'
question_types.append(question_type)
# 推荐食品
if self.check_words(self.food_qwds, question) and 'disease' in types:
deny_status = self.check_words(self.deny_words, question)
if deny_status:
question_type = 'disease_not_food'
else:
question_type = 'disease_do_food'
question_types.append(question_type)
#已知食物找疾病
if self.check_words(self.food_qwds+self.cure_qwds, question) and 'food' in types:
deny_status = self.check_words(self.deny_words, question)
if deny_status:
question_type = 'food_not_disease'
else:
question_type = 'food_do_disease'
question_types.append(question_type)
# 推荐药品
if self.check_words(self.drug_qwds, question) and 'disease' in types:
question_type = 'disease_drug'
question_types.append(question_type)
# 药品治啥病
if self.check_words(self.cure_qwds, question) and 'drug' in types:
question_type = 'drug_disease'
question_types.append(question_type)
# 疾病接受检查项目
if self.check_words(self.check_qwds, question) and 'disease' in types:
question_type = 'disease_check'
question_types.append(question_type)
# 已知检查项目查相应疾病
if self.check_words(self.check_qwds+self.cure_qwds, question) and 'check' in types:
question_type = 'check_disease'
question_types.append(question_type)
# 症状防御
if self.check_words(self.prevent_qwds, question) and 'disease' in types:
question_type = 'disease_prevent'
question_types.append(question_type)
# 疾病医疗周期
if self.check_words(self.lasttime_qwds, question) and 'disease' in types:
question_type = 'disease_lasttime'
question_types.append(question_type)
# 疾病治疗方式
if self.check_words(self.cureway_qwds, question) and 'disease' in types:
question_type = 'disease_cureway'
question_types.append(question_type)
# 疾病治愈可能性
if self.check_words(self.cureprob_qwds, question) and 'disease' in types:
question_type = 'disease_cureprob'
question_types.append(question_type)
# 疾病易感染人群
if self.check_words(self.easyget_qwds, question) and 'disease' in types :
question_type = 'disease_easyget'
question_types.append(question_type)
# 若没有查到相关的外部查询信息，那么则将该疾病的描述信息返回
if question_types == [] and 'disease' in types:
question_types = ['disease_desc']
# 若没有查到相关的外部查询信息，那么则将该疾病的描述信息返回
if question_types == [] and 'symptom' in types:
question_types = ['symptom_disease']
# 将多个分类结果进行合并处理，组装成一个字典
data['question_types'] = question_types
return data
'''构造词对应的类型'''
def build_wdtype_dict(self):
wd_dict = dict()
for wd in self.region_words:
wd_dict[wd] = []
if wd in self.disease_wds:
wd_dict[wd].append('disease')
if wd in self.department_wds:
wd_dict[wd].append('department')
if wd in self.check_wds:
wd_dict[wd].append('check')
if wd in self.drug_wds:
wd_dict[wd].append('drug')
if wd in self.food_wds:
wd_dict[wd].append('food')
if wd in self.symptom_wds:
wd_dict[wd].append('symptom')
if wd in self.producer_wds:
wd_dict[wd].append('producer')
return wd_dict
'''构造actree，加速过滤'''
def build_actree(self, wordlist):
actree = ahocorasick.Automaton()
for index, word in enumerate(wordlist):
actree.add_word(word, (index, word))
actree.make_automaton()
return actree
'''问句过滤'''
def check_medical(self, question):
region_wds = []
for i in self.region_tree.iter(question):
wd = i[1][1]
region_wds.append(wd)
stop_wds = []
for wd1 in region_wds:
for wd2 in region_wds:
if wd1 in wd2 and wd1 != wd2:
stop_wds.append(wd1)
final_wds = [i for i in region_wds if i not in stop_wds]
final_dict = {
i:self.wdtype_dict.get(i) for i in final_wds}
return final_dict
'''基于特征词进行分类'''
def check_words(self, wds, sent):
for wd in wds:
if wd in sent:
return True
return False
if __name__ == '__main__':
handler = QuestionClassifier()
while 1:
question = input('input an question:')
data = handler.classify(question)
print(data)
2.4.2 question_parser.py question analysis
#!/usr/bin/env python3
# coding: utf-8
class QuestionPaser:
'''构建实体节点'''
def build_entitydict(self, args):
entity_dict = {
}
for arg, types in args.items():
for type in types:
if type not in entity_dict:
entity_dict[type] = [arg]
else:
entity_dict[type].append(arg)
return entity_dict
'''解析主函数'''
def parser_main(self, res_classify):
args = res_classify['args']
entity_dict = self.build_entitydict(args)
question_types = res_classify['question_types']
sqls = []
for question_type in question_types:
sql_ = {
}
sql_['question_type'] = question_type
sql = []
if question_type == 'disease_symptom':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'symptom_disease':
sql = self.sql_transfer(question_type, entity_dict.get('symptom'))
elif question_type == 'disease_cause':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'disease_acompany':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'disease_not_food':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'disease_do_food':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'food_not_disease':
sql = self.sql_transfer(question_type, entity_dict.get('food'))
elif question_type == 'food_do_disease':
sql = self.sql_transfer(question_type, entity_dict.get('food'))
elif question_type == 'disease_drug':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'drug_disease':
sql = self.sql_transfer(question_type, entity_dict.get('drug'))
elif question_type == 'disease_check':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'check_disease':
sql = self.sql_transfer(question_type, entity_dict.get('check'))
elif question_type == 'disease_prevent':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'disease_lasttime':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'disease_cureway':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'disease_cureprob':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'disease_easyget':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
elif question_type == 'disease_desc':
sql = self.sql_transfer(question_type, entity_dict.get('disease'))
if sql:
sql_['sql'] = sql
sqls.append(sql_)
return sqls
'''针对不同的问题，分开进行处理'''
def sql_transfer(self, question_type, entities):
if not entities:
return []
# 查询语句
sql = []
# 查询疾病的原因
if question_type == 'disease_cause':
sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.cause".format(i) for i in entities]
# 查询疾病的防御措施
elif question_type == 'disease_prevent':
sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.prevent".format(i) for i in entities]
# 查询疾病的持续时间
elif question_type == 'disease_lasttime':
sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.cure_lasttime".format(i) for i in entities]
# 查询疾病的治愈概率
elif question_type == 'disease_cureprob':
sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.cured_prob".format(i) for i in entities]
# 查询疾病的治疗方式
elif question_type == 'disease_cureway':
sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.cure_way".format(i) for i in entities]
# 查询疾病的易发人群
elif question_type == 'disease_easyget':
sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.easy_get".format(i) for i in entities]
# 查询疾病的相关介绍
elif question_type == 'disease_desc':
sql = ["MATCH (m:Disease) where m.name = '{0}' return m.name, m.desc".format(i) for i in entities]
# 查询疾病有哪些症状
elif question_type == 'disease_symptom':
sql = ["MATCH (m:Disease)-[r:has_symptom]->(n:Symptom) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
# 查询症状会导致哪些疾病
elif question_type == 'symptom_disease':
sql = ["MATCH (m:Disease)-[r:has_symptom]->(n:Symptom) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
# 查询疾病的并发症
elif question_type == 'disease_acompany':
sql1 = ["MATCH (m:Disease)-[r:acompany_with]->(n:Disease) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql2 = ["MATCH (m:Disease)-[r:acompany_with]->(n:Disease) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql = sql1 + sql2
# 查询疾病的忌口
elif question_type == 'disease_not_food':
sql = ["MATCH (m:Disease)-[r:no_eat]->(n:Food) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
# 查询疾病建议吃的东西
elif question_type == 'disease_do_food':
sql1 = ["MATCH (m:Disease)-[r:do_eat]->(n:Food) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql2 = ["MATCH (m:Disease)-[r:recommand_eat]->(n:Food) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql = sql1 + sql2
# 已知忌口查疾病
elif question_type == 'food_not_disease':
sql = ["MATCH (m:Disease)-[r:no_eat]->(n:Food) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
# 已知推荐查疾病
elif question_type == 'food_do_disease':
sql1 = ["MATCH (m:Disease)-[r:do_eat]->(n:Food) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql2 = ["MATCH (m:Disease)-[r:recommand_eat]->(n:Food) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql = sql1 + sql2
# 查询疾病常用药品－药品别名记得扩充
elif question_type == 'disease_drug':
sql1 = ["MATCH (m:Disease)-[r:common_drug]->(n:Drug) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql2 = ["MATCH (m:Disease)-[r:recommand_drug]->(n:Drug) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql = sql1 + sql2
# 已知药品查询能够治疗的疾病
elif question_type == 'drug_disease':
sql1 = ["MATCH (m:Disease)-[r:common_drug]->(n:Drug) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql2 = ["MATCH (m:Disease)-[r:recommand_drug]->(n:Drug) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
sql = sql1 + sql2
# 查询疾病应该进行的检查
elif question_type == 'disease_check':
sql = ["MATCH (m:Disease)-[r:need_check]->(n:Check) where m.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
# 已知检查查询疾病
elif question_type == 'check_disease':
sql = ["MATCH (m:Disease)-[r:need_check]->(n:Check) where n.name = '{0}' return m.name, r.name, n.name".format(i) for i in entities]
return sql
if __name__ == '__main__':
handler = QuestionPaser()
2.4.3 answer_search.py answer search
#!/usr/bin/env python3
# coding: utf-8
from py2neo import Graph
class AnswerSearcher:
def __init__(self):
# self.g = Graph(
# host="127.0.0.1",
# http_port=7474,
# user="neo4j",
# password="123qwe")
self.g = Graph('http://localhost:7474/', auth=("neo4j", "123qwe"))
self.num_limit = 20
'''执行cypher查询，并返回相应结果'''
def search_main(self, sqls):
final_answers = []
for sql_ in sqls:
question_type = sql_['question_type']
queries = sql_['sql']
answers = []
for query in queries:
ress = self.g.run(query).data()
answers += ress
final_answer = self.answer_prettify(question_type, answers)
if final_answer:
final_answers.append(final_answer)
return final_answers
'''根据对应的qustion_type，调用相应的回复模板'''
def answer_prettify(self, question_type, answers):
final_answer = []
if not answers:
return ''
if question_type == 'disease_symptom':
desc = [i['n.name'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}的症状包括：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'symptom_disease':
desc = [i['m.name'] for i in answers]
subject = answers[0]['n.name']
final_answer = '症状{0}可能染上的疾病有：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_cause':
desc = [i['m.cause'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}可能的成因有：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_prevent':
desc = [i['m.prevent'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}的预防措施包括：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_lasttime':
desc = [i['m.cure_lasttime'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}治疗可能持续的周期为：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_cureway':
desc = [';'.join(i['m.cure_way']) for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}可以尝试如下治疗：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_cureprob':
desc = [i['m.cured_prob'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}治愈的概率为（仅供参考）：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_easyget':
desc = [i['m.easy_get'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}的易感人群包括：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_desc':
desc = [i['m.desc'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0},熟悉一下：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_acompany':
desc1 = [i['n.name'] for i in answers]
desc2 = [i['m.name'] for i in answers]
subject = answers[0]['m.name']
desc = [i for i in desc1 + desc2 if i != subject]
final_answer = '{0}的症状包括：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_not_food':
desc = [i['n.name'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}忌食的食物包括有：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_do_food':
do_desc = [i['n.name'] for i in answers if i['r.name'] == '宜吃']
recommand_desc = [i['n.name'] for i in answers if i['r.name'] == '推荐食谱']
subject = answers[0]['m.name']
final_answer = '{0}宜食的食物包括有：{1}\n推荐食谱包括有：{2}'.format(subject, ';'.join(list(set(do_desc))[:self.num_limit]), ';'.join(list(set(recommand_desc))[:self.num_limit]))
elif question_type == 'food_not_disease':
desc = [i['m.name'] for i in answers]
subject = answers[0]['n.name']
final_answer = '患有{0}的人最好不要吃{1}'.format('；'.join(list(set(desc))[:self.num_limit]), subject)
elif question_type == 'food_do_disease':
desc = [i['m.name'] for i in answers]
subject = answers[0]['n.name']
final_answer = '患有{0}的人建议多试试{1}'.format('；'.join(list(set(desc))[:self.num_limit]), subject)
elif question_type == 'disease_drug':
desc = [i['n.name'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}通常的使用的药品包括：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'drug_disease':
desc = [i['m.name'] for i in answers]
subject = answers[0]['n.name']
final_answer = '{0}主治的疾病有{1},可以试试'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'disease_check':
desc = [i['n.name'] for i in answers]
subject = answers[0]['m.name']
final_answer = '{0}通常可以通过以下方式检查出来：{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
elif question_type == 'check_disease':
desc = [i['m.name'] for i in answers]
subject = answers[0]['n.name']
final_answer = '通常可以通过{0}检查出来的疾病有{1}'.format(subject, '；'.join(list(set(desc))[:self.num_limit]))
return final_answer
if __name__ == '__main__':
searcher = AnswerSearcher()
2.4.4 chatbot_graph.py answer questions
#!/usr/bin/env python3
# coding: utf-8
from question_classifier import *
from question_parser import *
from answer_search import *
'''问答类'''
class ChatBotGraph:
def __init__(self):
self.classifier = QuestionClassifier()
self.parser = QuestionPaser()
self.searcher = AnswerSearcher()
def chat_main(self, sent):
answer = '您好，我是小勇医药智能助理，希望可以帮到您。如果没答上来，可联系https://liuhuanyong.github.io/。祝您身体棒棒！'
res_classify = self.classifier.classify(sent)
if not res_classify:
return answer
res_sql = self.parser.parser_main(res_classify)
final_answers = self.searcher.search_main(res_sql)
if not final_answers:
return answer
else:
return '\n'.join(final_answers)
if __name__ == '__main__':
handler = ChatBotGraph()
while 1:
question = input('用户:')
answer = handler.chat_main(question)
print('医生:', answer)
Then integrate the statements in the chatbot_graph.py file, and successfully run the query disease health.
references
【1】https://blog.csdn.net/weixin_44023658/article/details/112503294?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522166964389816782425686367%2522%252C%2522scm%2522%253A%252220140713.130102334…%2522%257D&request_id=166964389816782425686367&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2alltop_positive~default-1-112503294-null-null.142v67control,201v3add_ask,213v2t3_esquery_v2&utm_term=%E7%9F%A5%E8%AF%86%E5%9B%BE%E8%B0%B1&spm=1018.2226.3001.4187
【2】https://blog.csdn.net/zeroheitao/article/details/122925845
【3】