复刻python知识图谱

也算是个中间版：https://zhuanlan.zhihu.com/p/243211697?utm_source=qq

主要思想是取主语和宾语为实体节点，而将谓语存为关系边，然后对应的存到pandas的dataframe里。至于显示出来，使用networkx，plt来。
代码

import re
import pandas as pd
import bs4
import requests
import spacy
from spacy import displacy
nlp = spacy.load('en_core_web_sm')

from spacy.matcher import Matcher
from spacy.tokens import Span
import networkx as nx

import matplotlib.pyplot as plt
from tqdm import tqdm
pd.set_option('display.max_colwidth', 200)

candidate_sentences = pd.read_csv("wiki_sentences_v2.csv")

"""

print(candidate_sentences.shape)
print(candidate_sentences['sentence'].sample(5))

doc = nlp("the music rights were sold for ₹150 million .")

for tok in doc:
    print(tok.text, "...", tok.dep_)
"""


def get_entities(sent):
    ## chunk 1
    # 我在这个块中定义了一些空变量。prv tok dep和prv tok text将分别保留句子中前一个单词和前一个单词本身的依赖标签。前缀和修饰符将保存与主题或对象相关的文本。
    ent1 = ""
    ent2 = ""

    prv_tok_dep = ""  # dependency tag of previous token in the sentence
    prv_tok_text = ""  # previous token in the sentence

    prefix = ""
    modifier = ""

    #############################################################

    for tok in nlp(sent):
        ## chunk 2
        # 接下来，我们将遍历句子中的记号。我们将首先检查标记是否为标点符号。如果是，那么我们将忽略它并转移到下一个令牌。如果标记是复合单词的一部分(dependency tag = compound)，我们将把它保存在prefix变量中。复合词是由多个单词组成一个具有新含义的单词(例如“Football Stadium”, “animal lover”)。
        # 当我们在句子中遇到主语或宾语时，我们会加上这个前缀。我们将对修饰语做同样的事情，例如“nice shirt”, “big house”

        # if token is a punctuation mark then move on to the next token
        if tok.dep_ != "punct":
            # check: token is a compound word or not
            if tok.dep_ == "compound":
                prefix = tok.text
                # if the previous word was also a 'compound' then add the current word to it
                if prv_tok_dep == "compound":
                    prefix = prv_tok_text + " " + tok.text

            # check: token is a modifier or not
            if tok.dep_.endswith("mod") == True:
                modifier = tok.text
                # if the previous word was also a 'compound' then add the current word to it
                if prv_tok_dep == "compound":
                    modifier = prv_tok_text + " " + tok.text

            ## chunk 3
            # 在这里，如果令牌是主语，那么它将作为ent1变量中的第一个实体被捕获。变量如前缀，修饰符，prv tok dep，和prv tok文本将被重置。
            if tok.dep_.find("subj") == True:
                ent1 = modifier + " " + prefix + " " + tok.text
                prefix = ""
                modifier = ""
                prv_tok_dep = ""
                prv_tok_text = ""

                ## chunk 4
            # 在这里，如果令牌是宾语，那么它将被捕获为ent2变量中的第二个实体。变量，如前缀，修饰符，prv tok dep，和prv tok文本将再次被重置。
            if tok.dep_.find("obj") == True:
                ent2 = modifier + " " + prefix + " " + tok.text

            ## chunk 5
            # 一旦我们捕获了句子中的主语和宾语，我们将更新前面的标记和它的依赖标记。
            # update variables
            prv_tok_dep = tok.dep_
            prv_tok_text = tok.text
    #############################################################

    return [ent1.strip(), ent2.strip()]


def get_relation(sent):
    doc = nlp(sent)
   # Matcher class object
    matcher = Matcher(nlp.vocab)
   #define the pattern
    pattern = [{'DEP':'ROOT'}, {'DEP':'prep','OP':"?"}, {'DEP':'agent','OP':"?"}, {'POS':'ADJ','OP':"?"}]
    matcher.add("matching_1", [pattern])
    matches = matcher(doc)
    k = len(matches) - 1
    span = doc[matches[k][1]:matches[k][2]]
    return(span.text)


entity_pairs = []
for i in tqdm(candidate_sentences["sentence"]):
    entity_pairs.append(get_entities(i))

print(entity_pairs[10:20])
get_relation("John completed the task")
relations = [get_relation(i) for i in tqdm(candidate_sentences['sentence'])]
print(pd.Series(relations).value_counts()[:50])

# extract subject
source = [i[0] for i in entity_pairs]
# extract object
target = [i[1] for i in entity_pairs]


kg_df = pd.DataFrame({'source':source, 'target':target, 'edge':relations})
"""

# create a directed-graph from a dataframe
G=nx.from_pandas_edgelist(kg_df, "source", "target", edge_attr=True, create_using=nx.MultiDiGraph())
plt.figure(figsize=(12,12))
pos = nx.spring_layout(G)
nx.draw(G, with_labels=True, node_color='skyblue', edge_cmap=plt.cm.Blues, pos = pos)
plt.show()
"""
G=nx.from_pandas_edgelist(
    kg_df[kg_df['edge']=="released in"],
    "source",
    "target",
    edge_attr=True,
    create_using=nx.MultiDiGraph())
plt.figure(figsize=(12,12))
pos = nx.spring_layout(G, k = 0.5) # k regulates the distance between nodes
nx.draw(G, with_labels=True, node_color='skyblue', node_size=1500, edge_cmap=plt.cm.Blues, pos = pos)
plt.show()

截图

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