【知识图谱】实体对齐(1)

　　实体对齐、实体消歧作为知识图谱中的重要一环，最近感兴趣找了些资料记录分享一下，本篇将实现一种简单的实体对齐的方法，主要思路是利用tfidf计算文本相似度，本文准备了两个测试数据集，entity_list.csv是50个实体，valid_data.csv是需要消歧的语句。

知识库entity_list的格式如下

需要进行实体消歧的语句valid_data如下：

 具体的实现步骤如下

1.导入数据以及相关库

import jieba
import collections
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
entity_data = pd.read_csv('data/entity_disambiguation/entity_list.csv', encoding = 'utf-8')
valid_data = pd.read_csv('data/entity_disambiguation/valid_data.csv', encoding = 'gb18030')

2.建立待消歧词列表

s = ''
keywords = []
for i in entity_data['entity_name'].values.tolist():
    s +=i + '|'
for k,v in collections.Counter(s.split('|')).items():
    if v>1:#频次大于1的词才会有歧义，此处进行筛选
        keywords.append(k)

3.生成tfidf矩阵

rain_sentence = []
for i in entity_data['desc'].values:
    train_sentence.append(' '.join(jieba.cut(i)))
vec = TfidfVectorizer()
X = vec.fit_transform(train_sentence)

4.获取关键词所属entity_id

def get_entityid(sentence):
    id_start = 1001
    a_list = [' '.join(jieba.cut(sentence))]
    res = cosine_similarity(vec.transform(a_list),X)[0]#[[x,x,x,x]]格式
    top_idx = np.argsort(res)[-1]#获取相似度最大文本所在位置索引
    return id_start + top_idx

5.循环计算各词索引并返回

result_data = []
neighbor_sentence = ''
for sen in valid_data['sentence']:
    res = []
    for keyword in keywords:
        if keyword in sen:
            len_key = len(keyword)
            ss = ''
            for i in range(len(sen)-len_key+1):
                if sen[i:i+len_key] == keyword:
                    s = str(i) + '-' +str(i+len_key) + ':'
                    if i > 10 and i + len_key < len(sen) - 9:
                        neighbor_sentence = sen[i - 10:i + len_key + 9]
                    elif i < 10:
                        neighbor_sentence = sen[:20]
                    elif i + len_key > len(sen) - 9:
                        neighbor_sentence = sen[-20:]
                    s += str(get_entityid(neighbor_sentence))  # 拿到 x-x:id
                    ss += s + '|'  # 拿到 x-x:id|x-x:id
            res.append(ss[:-1])  #去除最后一个'|'
    result_data.append(res)

输出格式：“实体起始位坐标-实体结束位坐标：实体序号”以“|”分隔的字符串

参考引用：

https://blog.csdn.net/lt326030434/article/details/88668764?utm_medium=distribute.pc_relevant.none-task-blog-baidujs-1

https://github.com/Maginaaa/NamedEntityDisambiguation