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{
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{
"cell_type": "code",
"execution_count": null,
"id": "c49abf54-35c7-4b82-aa31-a155633c3327",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "43644952-bca3-4060-af76-3d5a8357be06",
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"import pandas as pd\n",
"import bs4\n",
"import requests\n",
"import spacy\n",
"from spacy import displacy\n",
"nlp = spacy.load('en_core_web_sm')\n",
"\n",
"from spacy.matcher import Matcher \n",
"from spacy.tokens import Span \n",
"\n",
"import networkx as nx\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from tqdm import tqdm\n",
"\n",
"pd.set_option('display.max_colwidth', 200)\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b73f085-2b8b-4f48-b26c-2da5fb22c9f2",
"metadata": {},
"outputs": [],
"source": [
"# import wikipedia sentences\n",
"candidate_sentences = pd.read_csv(\"../input/wiki-sentences1/wiki_sentences_v2.csv\")\n",
"candidate_sentences.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1bd9de52-e1bc-46a6-9f52-e90969ed9f0c",
"metadata": {},
"outputs": [],
"source": [
"def get_entities(sent):\n",
" ## chunk 1\n",
" ent1 = \"\"\n",
" ent2 = \"\"\n",
"\n",
" prv_tok_dep = \"\" # dependency tag of previous token in the sentence\n",
" prv_tok_text = \"\" # previous token in the sentence\n",
"\n",
" prefix = \"\"\n",
" modifier = \"\"\n",
"\n",
" #############################################################\n",
" \n",
" for tok in nlp(sent):\n",
" ## chunk 2\n",
" # if token is a punctuation mark then move on to the next token\n",
" if tok.dep_ != \"punct\":\n",
" # check: token is a compound word or not\n",
" if tok.dep_ == \"compound\":\n",
" prefix = tok.text\n",
" # if the previous word was also a 'compound' then add the current word to it\n",
" if prv_tok_dep == \"compound\":\n",
" prefix = prv_tok_text + \" \"+ tok.text\n",
" \n",
" # check: token is a modifier or not\n",
" if tok.dep_.endswith(\"mod\") == True:\n",
" modifier = tok.text\n",
" # if the previous word was also a 'compound' then add the current word to it\n",
" if prv_tok_dep == \"compound\":\n",
" modifier = prv_tok_text + \" \"+ tok.text\n",
" \n",
" ## chunk 3\n",
" if tok.dep_.find(\"subj\") == True:\n",
" ent1 = modifier +\" \"+ prefix + \" \"+ tok.text\n",
" prefix = \"\"\n",
" modifier = \"\"\n",
" prv_tok_dep = \"\"\n",
" prv_tok_text = \"\" \n",
"\n",
" ## chunk 4\n",
" if tok.dep_.find(\"obj\") == True:\n",
" ent2 = modifier +\" \"+ prefix +\" \"+ tok.text\n",
" \n",
" ## chunk 5 \n",
" # update variables\n",
" prv_tok_dep = tok.dep_\n",
" prv_tok_text = tok.text\n",
" #############################################################\n",
"\n",
" return [ent1.strip(), ent2.strip()]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11bec388-fdb8-4823-9049-aa4cf328eba6",
"metadata": {},
"outputs": [],
"source": [
"entity_pairs = []\n",
"\n",
"for i in tqdm(candidate_sentences[\"sentence\"]):\n",
" entity_pairs.append(get_entities(i))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "02f56072-ae65-4b15-a3b6-674701040568",
"metadata": {},
"outputs": [],
"source": [
"def get_relation(sent):\n",
"\n",
" doc = nlp(sent)\n",
"\n",
" # Matcher class object \n",
" matcher = Matcher(nlp.vocab)\n",
"\n",
" #define the pattern \n",
" pattern = [{'DEP':'ROOT'}, \n",
" {'DEP':'prep','OP':\"?\"},\n",
" {'DEP':'agent','OP':\"?\"}, \n",
" {'POS':'ADJ','OP':\"?\"}] \n",
"\n",
" matcher.add(\"matching_1\", None, pattern) \n",
"\n",
" matches = matcher(doc)\n",
" k = len(matches) - 1\n",
"\n",
" span = doc[matches[k][1]:matches[k][2]] \n",
"\n",
" return(span.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee3a774f-9f2d-4a4c-a77a-04bc420d4864",
"metadata": {},
"outputs": [],
"source": [
"relations = [get_relation(i) for i in tqdm(candidate_sentences['sentence'])]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c04581bb-46b5-48ce-bbe1-b465a789ad82",
"metadata": {},
"outputs": [],
"source": [
"# extract subject\n",
"source = [i[0] for i in entity_pairs]\n",
"\n",
"# extract object\n",
"target = [i[1] for i in entity_pairs]\n",
"\n",
"kg_df = pd.DataFrame({'source':source, 'target':target, 'edge':relations})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0fec1f2-d370-4d79-8a92-2ebdff2be420",
"metadata": {},
"outputs": [],
"source": [
"# create a directed-graph from a dataframe\n",
"G=nx.from_pandas_edgelist(kg_df, \"source\", \"target\", \n",
" edge_attr=True, create_using=nx.MultiDiGraph())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39b80dbe-f991-4e12-b0a1-4026344af82f",
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(12,12))\n",
"\n",
"pos = nx.spring_layout(G)\n",
"nx.draw(G, with_labels=True, node_color='skyblue', edge_cmap=plt.cm.Blues, pos = pos)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "be07f563-0b61-441f-bb24-a9e884eef1b8",
"metadata": {},
"outputs": [],
"source": [
"#https://www.kaggle.com/code/pavansanagapati/knowledge-graph-nlp-tutorial-bert-spacy-nltk"
]
}
],
"metadata": {
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"display_name": "Python 3 (ipykernel)",
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"language_info": {
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"file_extension": ".py",
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