前言
学习github上的项目代码，熟悉知识图谱构建流程。

---

知识图谱数据源

• 一部分数据爬取自网站
• 另一部分数据直接使用开源的Tushare，这部分数据可以直接构建实体

抽取网页数据代码

在原项目代码上修改了几处，加了一些注释。

import os
import csv
from lxml import etree

def extract(stockpage_dir, directors_csv):
    """Extract executive of the comnpany or stock

    Args:
        stockpage_dir: (str) the directory of stock pages
        executive_csv: (str) the full path of the CSV file to be saved
    """
    pages = map(lambda _: os.path.join(stockpage_dir, _), os.listdir(stockpage_dir))
    # os.listdir 获取指定目录中的所有文件名，返回文件名列表，无序
    # lambda表达式是一个匿名函数，适合与map、filter等函数结合使用
    # os.path.join 拼接文件路径，正常情况下拼接符是单斜杠，但在此处拼接符是双斜杠  "./data/stockpage\\000001.html"
    # map()函数接收两个参数，一个是函数，一个是Iterable，map将传入的函数依次作用到序列的每一个元素，并把结果作为新的Iterable返回
    pages = filter(lambda _: _.endswith('html'), pages)    # 过滤掉非html文件，返回由.html文件组成的新list
    headers = ['name', 'gender', 'age', 'code', 'jobs']

    with open(directors_csv, 'w', encoding='utf-8',newline="") as file_directors:
        file_directors_csv = csv.DictWriter(file_directors, headers)  # 创建DictWriter对象
        file_directors_csv.writeheader()  # 写入表头

        for page in pages:
            print(page)  # the full path of a stock page
            file_name = page.split(r'/')[-1]  # [".","data","stockpage\\×××.html"]，file_name取值为
            code = file_name.split('.')[0]  # ["stockpage\×××","html"]
            code=code.split('\\')[-1]  # 转义符\
            executives = []
            with open(page, 'r', encoding='gbk') as file_page:
                content = file_page.read()
                html = etree.HTML(content)  # etree.HTML用来解析字符串格式的HTML文档对象，将传进去的字符串转变成_Element对象。作为_Element对象，可以方便的使用getparent()、remove()、xpath()等方法。
                # 如果想通过xpath获取html源码中的内容，就要先将html源码转换成_Element对象，然后再使用xpath()方法进行解析。
                divs = html.xpath('//div[@id="ml_001"]//div[contains(@class, "person_table")]')  # xpath返回董事链接源码包含的元素列表

                for div in divs:
                    item = {}
                    item['name'] = div.xpath('.//thead/tr/td/h3/a/text()')[0].replace(',', '-')
                    item['jobs'] = div.xpath('.//thead/tr[1]/td[2]/text()')[0].replace(',', '/')
                    gender_age_education = div.xpath('.//thead/tr[2]/td[1]/text()')[0].split()
                    try:
                        item['gender'] = gender_age_education[0]
                        if item['gender'] not in ('男', '女'):
                            item['gender'] = 'null'  # null for unknown
                    except IndexError:
                        item['gender'] = 'null'

                    try:
                        item['age'] = gender_age_education[1].strip('岁')
                        try:
                            item['age'] = int(item['age'])
                        except ValueError:
                            item['age'] = -1  # -1 for unknown
                    except IndexError:
                        item['age'] = -1

                    item['code'] = code

                    executives.append(item)
            # write to csv file
            file_directors_csv.writerows(executives)

if __name__ == '__main__':
    stockpage_dir = './data/stockpage'
    directors_csv = './data/executive_prep.csv'
    extract(stockpage_dir, directors_csv)

上述代码利用xpath定位数据源网页文件元素，抽取出实体信息存储在csv文件中，与用requests、bs4包爬取网页信息方法不同。但如果想自己写爬虫代码的话，需要熟悉python语言，并且懂一点html相关的知识。抽取数据截图如下：

数据处理

从网站上爬取的数据虽然存储为csv文件中，但是需要处理，将每种实体单独存在一个csv文件中，这样在neo4j中可以直接生成实体比较方便，实体之间的关系可以抽取出来存在csv中，也可以利用py2neo.NodeMatcher查询实体，再利用Relationship生成关系。

在原项目上为mode为写模式的open函数添加了一个newline参数，这样生成的csv文件中不会包含多余的空行。

import os
import csv
import hashlib

# 加密算法主要用来生成实体id
def get_md5(string):
    """Get md5 according to the string
    """
    byte_string = string.encode("utf-8")
    md5 = hashlib.md5()  # 定义md5对象，用hashlib的md5算法加密数据
    md5.update(byte_string)  # 对字符串进行加密
    result = md5.hexdigest()  # 加密字符串
    return result

# 生成Person实体对应的csv文件
def build_executive(executive_prep, executive_import):
    """Create an 'executive' file in csv format that can be imported into Neo4j.
    format -> person_id:ID,name,gender,age:int,:LABEL
    label -> Person
    """
    print('Writing to {} file...'.format(executive_import.split('/')[-1]))
    with open(executive_prep, 'r', encoding='utf-8') as file_prep, \
        open(executive_import, 'w', encoding='utf-8',newline="") as file_import:
        file_prep_csv = csv.reader(file_prep, delimiter=',')  # 返回reader对象，可以遍历csv中的行，从csv文件中读取的每一行都以字符串列表的形式返回。
        file_import_csv = csv.writer(file_import, delimiter=',')  # 创建writer对象，传入列表对象

        headers = ['person_id:ID', 'name', 'gender', 'age:int', ':LABEL']
        file_import_csv.writerow(headers)      # 将列表中的元素依次附加在 csv 文件的下一行
        for i, row in enumerate(file_prep_csv):
            if i == 0 or len(row) < 3:   # 跳过表头和有缺失值的行
                continue
            info = [row[0], row[1], row[2]]
            # generate md5 according to 'name' 'gender' and 'age'
            info_id = get_md5('{},{},{}'.format(row[0], row[1], row[2]))
            info.insert(0, info_id)
            info.append('Person')  #[加密字符串、name、gender、age、"Person"]
            file_import_csv.writerow(info)
    print('- done.')

# 生成Company实体对应的csv文件
def build_stock(stock_industry_prep, stock_concept_prep, stock_import):
    """Create an 'stock' file in csv format that can be imported into Neo4j.
    format -> company_id:ID,name,code,:LABEL
    label -> Company,ST
    """
    print('Writing to {} file...'.format(stock_import.split('/')[-1]))
    stock = set()  # 'code,name'

    # 抽取股票代码和公司名称
    with open(stock_industry_prep, 'r', encoding='utf-8') as file_prep:
        file_prep_csv = csv.reader(file_prep, delimiter=',')
        for i, row in enumerate(file_prep_csv):
            if i == 0:  # 跳过表头
                continue
            code_name = '{},{}'.format(row[0], row[1].replace(' ', ''))
            stock.add(code_name)

    # 抽取股票代码和股票名称
    with open(stock_concept_prep, 'r', encoding='utf-8') as file_prep:
        file_prep_csv = csv.reader(file_prep, delimiter=',')
        for i, row in enumerate(file_prep_csv):
            if i == 0:
                continue
            code_name = '{},{}'.format(row[0], row[1].replace(' ', ''))
            stock.add(code_name)

    with open(stock_import, 'w', encoding='utf-8',newline="") as file_import:
        file_import_csv = csv.writer(file_import, delimiter=',')
        headers = ['stock_id:ID', 'name', 'code', ':LABEL']
        file_import_csv.writerow(headers)  # 写入表头
        for s in stock:
            split = s.split(',')
            ST = False  # ST flag，标识股票亏损状态
            states = ['*ST', 'ST', 'S*ST', 'SST']
            info = []
            for state in states:
                if split[1].startswith(state):  # 名票名称前缀中包含ST标识
                    ST = True
                    split[1] = split[1].replace(state, '')
                    info = [split[0], split[1], split[0], 'Company;ST']
                    break
                else:
                    info = [split[0], split[1], split[0], 'Company']
            file_import_csv.writerow(info)
    print('- done.')

# 生成Concept实体对应的csv文件
def build_concept(stock_concept_prep, concept_import):
    """Create an 'concept' file in csv format that can be imported into Neo4j.
    format -> concept_id:ID,name,:LABEL
    label -> Concept
    """
    print('Writing to {} file...'.format(concept_import.split('/')[-1]))
    with open(stock_concept_prep, 'r', encoding='utf-8') as file_prep, \
        open(concept_import, 'w', encoding='utf-8',newline="") as file_import:
        file_prep_csv = csv.reader(file_prep, delimiter=',')
        file_import_csv = csv.writer(file_import, delimiter=',')

        headers = ['concept_id:ID', 'name', ':LABEL']
        file_import_csv.writerow(headers)
        concepts = set()
        for i, row in enumerate(file_prep_csv):
            if i == 0:
                continue
            concepts.add(row[2])
        for concept in concepts:
            concept_id = get_md5(concept)
            new_row = [concept_id, concept, 'Concept']
            file_import_csv.writerow(new_row)
    print('- done.')

# 生成Industry实体对应的csv文件
def build_industry(stock_industry_prep, industry_import):
    """Create an 'industry' file in csv format that can be imported into Neo4j.
    format -> industry_id:ID,name,:LABEL
    label -> Industry
    """
    print('Write to {} file...'.format(industry_import.split('/')[-1]))
    with open(stock_industry_prep, 'r', encoding="utf-8") as file_prep, \
        open(industry_import, 'w', encoding='utf-8',newline="") as file_import:
        file_prep_csv = csv.reader(file_prep, delimiter=',')
        file_import_csv = csv.writer(file_import, delimiter=',')
        headers = ['industry_id:ID', 'name', ':LABEL']
        file_import_csv.writerow(headers)

        industries = set()
        for i, row in enumerate(file_prep_csv):
            if i == 0:
                continue
            industries.add(row[2])
        for industry in industries:
            industry_id = get_md5(industry)
            new_row = [industry_id, industry, 'Industry']
            file_import_csv.writerow(new_row)
    print('- done.')

# 生成 employ_of 关系对应的 csv 文件，title 是 employ_of 关系的属性
def build_executive_stock(executive_prep, relation_import):
    """Create an 'executive_stock' file in csv format that can be imported into Neo4j.
    format -> :START_ID,title,:END_ID,:TYPE
               person          stock
    type -> employ_of
    """
    with open(executive_prep, 'r', encoding='utf-8') as file_prep, \
        open(relation_import, 'w', encoding='utf-8',newline="") as file_import:
        file_prep_csv = csv.reader(file_prep, delimiter=',')
        file_import_csv = csv.writer(file_import, delimiter=',')
        headers = [':START_ID', 'jobs', ':END_ID', ':TYPE']
        file_import_csv.writerow(headers)

        for i, row in enumerate(file_prep_csv):
            if i == 0:
                continue
            # generate md5 according to 'name' 'gender' and 'age'，作为START_ID
            start_id = get_md5('{},{},{}'.format(row[0], row[1], row[2]))
            end_id = row[3]  # code
            relation = [start_id, row[4], end_id, 'employ_of']
            file_import_csv.writerow(relation)

# 生成industry_of关系对应的csv文件，这个关系没有属性
def build_stock_industry(stock_industry_prep, relation_import):
    """Create an 'stock_industry' file in csv format that can be imported into Neo4j.
    format -> :START_ID,:END_ID,:TYPE
               stock   industry
    type -> industry_of
    """
    with open(stock_industry_prep, 'r', encoding='utf-8') as file_prep, \
        open(relation_import, 'w', encoding='utf-8',newline="") as file_import:
        file_prep_csv = csv.reader(file_prep, delimiter=',')
        file_import_csv = csv.writer(file_import, delimiter=',')
        headers = [':START_ID', ':END_ID', ':TYPE']
        file_import_csv.writerow(headers)

        for i, row in enumerate(file_prep_csv):
            if i == 0:
                continue
            industry = row[2]
            start_id = row[0]  # code
            end_id = get_md5(industry)
            relation = [start_id, end_id, 'industry_of']
            file_import_csv.writerow(relation)

# 生成 concept_of 关系对应的csv文件，该关系同样没有属性
def build_stock_concept(stock_concept_prep, relation_import):
    """Create an 'stock_industry' file in csv format that can be imported into Neo4j.
    format -> :START_ID,:END_ID,:TYPE
               stock   concept
    type -> concept_of
    """
    with open(stock_concept_prep, 'r', encoding='utf-8') as file_prep, \
        open(relation_import, 'w', encoding='utf-8',newline="") as file_import:
        file_prep_csv = csv.reader(file_prep, delimiter=',')
        file_import_csv = csv.writer(file_import, delimiter=',')
        headers = [':START_ID', ':END_ID', ':TYPE']
        file_import_csv.writerow(headers)

        for i, row in enumerate(file_prep_csv):
            if i == 0:
                continue
            concept = row[2]
            start_id = row[0]  # code
            end_id = get_md5(concept)
            relation = [start_id, end_id, 'concept_of']
            file_import_csv.writerow(relation)


if __name__ == '__main__':
    import_path = 'data1/import'
    if not os.path.exists(import_path):
        os.makedirs(import_path)
    build_executive('data1/executive_prep.csv', 'data1/import/executive.csv')
    build_stock('data1/stock_industry_prep.csv', 'data1/stock_concept_prep.csv',
        'data1/import/stock.csv')
    build_concept('data1/stock_concept_prep.csv', 'data1/import/concept.csv')
    build_industry('data1/stock_industry_prep.csv', 'data1/import/industry.csv')

    build_executive_stock('data1/executive_prep.csv', 'data1/import/executive_stock.csv')
    build_stock_industry('data1/stock_industry_prep.csv', 'data1/import/stock_industry.csv')
    build_stock_concept('data1/stock_concept_prep.csv', 'data1/import/stock_concept.csv')

运行结果如下：

构建知识图谱

原作者直接将抽取好的实体和关系csv文件导入neo4j生成知识图谱，注意csv文件的存储路径就行。

思考

构建人的实体时，重名问题具体怎么解决？

总结

感觉这个项目是原作者的一个作业，原作者将它分享到github上了。他构建知识图谱的重点在爬取网页数据和处理数据上，处理好数据以后直接导入neo4j生成知识图谱了。