1、Neo4j 3.5.6（2019年6月25日）
2、Java 1.8.0_181
3、Annaconda 3
Neo4j安装：https://blog.csdn.net/gggggao/article/details/93595137
目的：学习并尝试构建知识图谱，选择时光网的电影排名做尝试。
时光网链接： http://www.mtime.com/top/movie/top100/
数据导入neo4j的方式有五种，这里采用最后一种方法（Neo4j-import）来进行导入
查看并分析网页源代码之后，确定爬取的节点和关系对象如下：
节点:
|----电影
|----index:ID
|----properity: rank, src, name, movie_en, year
|----label
|----导演
|----index:ID
|----properity: director
|----label
|----演员
|----index:ID
|----properity: actor
|----label
关系:
|----电影<--导演
|----start:START_ID
|----end:END_ID
|----properity: relation
|----type:TYPE
|----电影<--演员
|----start:START_ID
|----end:END_ID
|----properity: relation
|----type:TYPE
|----演员-->导演
|----start:START_ID
|----end:END_ID
|----properity: relation
|----type:TYPE
3.1.1 下载网页
本文采用BeautifulSoup来进行数据爬取，首先下载url的html文件
def download_page(url):
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.80 Safari/537.36'}
return requests.get(url, headers = headers).content3.1.2 解析网页
接着对下载的网页进行解析，得到全部电影信息的list，也就是movie_list
def parse_timesmovie_html(html):
soup = BeautifulSoup(html, features="lxml") #parse tool: BeautifulSoup
movie_list = [] # movie_node list: sava all the movie info into the list
movie_list_soup = soup.find('ul', attrs={'id':'asyncRatingRegion'}) #find body
#get the data of: rank, movie_name, src, director, movie_actor[]; then save in the
for movie_li in movie_list_soup.find_all('li'):
rank = movie_li.find('div', attrs={'class': 'number'}).getText() #rank of movie
movie_info = movie_li.find('div', attrs={'class': 'mov_con'})
movie_temp = movie_info.find('a').getText().split("\xa0") #name of movie
movie_name = movie_temp[0]
year = movie_temp[1].split(" ")[-1].replace('(','').replace(')','') #release date of movie
movie_en_name = movie_temp[1][:-7] #English name of movie
#print(movie_en_name)
src = movie_info.find('a')['href'] #src of movie
# extract the infomation of director&actor
person_info = movie_info.select('p')
# director of movie
director = person_info[0].find('a').string
# actors of movie
movie_actor = []
for act in person_info[1].find_all('a'):
movie_actor.append(act.string)
movie_info_all = {'rank': rank,
'src': src,
'movie_name': movie_name,
'movie_en_name': movie_en_name,
'year': year,
'director': director,
'actor': movie_actor
}
movie_list.append(movie_info_all)
return movie_list3.1.3 处理重复节点
我们知道，会存在一个导演拍了多部电影，或者一个演员演了多部电影的情况存在，因此，我们需要对可能会重复的节点（导演和演员）进行处理，并返回节点的字典
def handle_entity(person_list):
director_list, actor_list = [], []
dir_dic, actor_dic = {}, {}
# separate infomation from person_list
for elm in person_list:
director_list.append(elm['director'])
actor_list.extend(elm['actor'])
#utilize set() to remove duplicate data from the list[]
director_set=set(director_list)
actor_set = set(actor_list)
#save data to the dictionary
for index,value in enumerate(director_set):
dir_dic[index] = value
for index,value in enumerate(actor_set):
actor_dic[index] = value
return dir_dic, actor_dic3.2.1 生成节点csv文件
根据上面的分析我们要生成3个节点的csv文件，分别是
电影：times_movie_entity.csv
导演：times_director_entity.csv
演员：times_actor_entity.csv
代码如下所示
#times_movie_entity.csv
def save_movie_entity(movie_entity_list):
data = pd.DataFrame({'index:ID': [index+1000 for index in range(len(movie_entity_list))],
'rank': [movie['rank'] for movie in movie_entity_list],
'src': [movie['src'] for movie in movie_entity_list],
'name': [movie['movie_name'] for movie in movie_entity_list],
'movie_en': [movie['movie_en_name'] for movie in movie_entity_list],
'year': [movie['year'] for movie in movie_entity_list],
':LABEL': 'movie'
})
data.to_csv(r'./times_movie_entity.csv', index=False)
#times_director_entity.csv
def save_director_entity(director_entity_dic):
data = pd.DataFrame({'index:ID': [item+2000 for item in director_entity_dic.keys()],
'director': [val for val in director_entity_dic.values()],
':LABEL': 'director'
})
data.to_csv(r'./times_director_entity.csv', index=False)
#times_actor_entity.csv
def save_actor_entity(actor_entity_dic):
data = pd.DataFrame({'index:ID': [item+3000 for item in actor_entity_dic.keys()],
'actor': [val for val in actor_entity_dic.values()],
':LABEL': 'actor'
})
data.to_csv(r'./times_actor_entity.csv', index=False)注意：
为了很好的区分三个节点，我们对他们的index进行处理，分别加了1000、2000和3000，也就是
节点文档格式规则 [2]
生成的times_movie_entity.csv文件格式见
3.2.2 生成关系csv文件
根据3.2.1生成的节点文件，我们继续生成它们的3个关系文件，分别是
电影<--导演：times_movie_director_relationship.csv
电影<--演员：times_movie_actor_relationship.csv
演员-->导演：times_director_actor_relationship.csv
代码如下所示
#times_movie_director_relationship.csv
def save_movie_director_relationship(movie_list, movie_dic, director_dic):
data = pd.DataFrame({':START_ID': [list(director_dic.keys())[list(director_dic.values()).index(info['director'])]+2000 for info in movie_list],
':END_ID': [list(movie_dic.keys())[list(movie_dic.values()).index(info['movie_name'])] for info in movie_list],
'relation': 'directed',
':TYPE':'directed'
})
data.to_csv(r'./times_movie_director_relationship.csv', index=False)
#times_movie_actor_relationship.csv
def save_movie_actor_relationship(movie_list, movie_dic, actor_dict):
relation_list = []
for movie in movie_list:
for actor_info in movie['actor']:
actor_index = list(actor_dict.keys())[list(actor_dict.values()).index(actor_info)]
movie_index = list(movie_dic.keys())[list(movie_dic.values()).index(movie['movie_name'])]
info = {
'actor_index': actor_index,
'movie_index': movie_index
}
relation_list.append(info)
data = pd.DataFrame({':START_ID': [relation['actor_index']+3000 for relation in relation_list],
':END_ID': [relation['movie_index'] for relation in relation_list],
'relation': 'acted',
':TYPE': 'acted'
})
data.to_csv(r'./times_movie_actor_relationship.csv', index=False)
#times_director_actor_relationship.csv
def save_director_actor_relationship(movie_list, director_dic, actor_dict):
relation_list = []
for movie in movie_list:
for actor_info in movie['actor']:
actor_index = list(actor_dict.keys())[list(actor_dict.values()).index(actor_info)]
dir_index = list(director_dic.keys())[list(director_dic.values()).index(movie['director'])]
info = {
'actor_index': actor_index,
'dir_index': dir_index
}
relation_list.append(info)
data = pd.DataFrame({':START_ID': [relation['dir_index']+2000 for relation in relation_list],
':END_ID': [relation['actor_index']+3000 for relation in relation_list],
'relation': 'cooperate',
':TYPE': 'cooperate'
})
data.to_csv(r'./times_director_actor_relationship.csv', index=False)生成的times_director_actor_relationship.csv文件格式见
3.3.1 准备工作
在导入之前要先关闭neo4j服务
以管理员身份打卡命令提示符
查看neo4j状态，如果是运行状态则将其关闭
3.3.2 生成图谱
服务关闭之后我们导入文件，如下图所示，
命令如下所示：
neo4j-admin import --mode=csv --database=movie.db --nodes D:\TestCodes\Python\CrawlerDemos\Neo4jDemo2\times_actor_entity.csv --nodes D:\TestCodes\Python\CrawlerDemos\Neo4jDemo2\times_director_entity.csv --nodes D:\TestCodes\Python\CrawlerDemos\Neo4jDemo2\times_movie_entity.csv --relationships D:\TestCodes\Python\CrawlerDemos\Neo4jDemo2\times_director_actor_relationship.csv --relationships D:\TestCodes\Python\CrawlerDemos\Neo4jDemo2\times_movie_actor_relationship.csv --relationships D:\TestCodes\Python\CrawlerDemos\Neo4jDemo2\times_movie_director_relationship.csv导入成功
命令成功运行之后多了一个neo4j数据库：movie.db里面存放着我们的刚刚成功存储的数据。
为了能把数据加载出来，我们接着进conf设置默认加载的数据库
打开neo4j.conf，输入 dbms.active_database=movie.db，如下所示
最后我们输入Cyper语句查询宫崎骏图谱，Cyper语句如下所示：
match m=(:director{director:'宫崎骏'})-[*..1]-() return m
查询结果如图所示
代码github地址：https://github.com/Gao0505/KnowledgeGraph/tree/master/TimesMovieKG
[1] https://blog.csdn.net/BF02jgtRS00XKtCx/article/details/89078048
[2] https://blog.csdn.net/sinat_25479505/article/details/80996402
[3] https://blog.csdn.net/Chen18125/article/details/84101458