""" Process raw T-Rex file. mkdir data_raw cd data_raw wget https://figshare.com/ndownloader/files/8760241 unzip 8760241 cd ../ """ import json import string import re import os from glob import glob from tqdm import tqdm import pandas as pd # process raw data if not os.path.exists('data/t_rex.raw.jsonl'): os.makedirs('data', exist_ok=True) f_writer = open('data/t_rex.raw.jsonl', 'w') for i in tqdm(glob("data_raw/*.json")): with open(i) as f: data = json.load(f) for _data in data: for triple in _data['triples']: p = triple['predicate']['surfaceform'] if p is None: p = os.path.basename(triple['predicate']['uri']) o = triple['object']['surfaceform'] s = triple['subject']['surfaceform'] if o is None or s is None: input(triple) out = {"predicate": p, "object": o, "subject": s, "title": _data["title"], "text": _data["text"]} f_writer.write(json.dumps(out) + "\n") f_writer.close() # apply filtering to remove noisy instances stopwords = ["he", "she", "they", "it"] list_alnum = string.ascii_lowercase + '0123456789 ' def filtering(entry): def _subfilter(token): if len(re.findall(rf'[^{list_alnum}]+', token)) != 0: return False if token in stopwords: return False if token.startswith("www"): return False if token.startswith("."): return False if token.startswith(","): return False if token.startswith("$"): return False if token.startswith("+"): return False if token.startswith("#"): return False return True if not _subfilter(entry["object"].lower()): return False if not _subfilter(entry["subject"].lower()): return False if entry['object'].islower() and entry['subject'].islower(): return False return True with open(f"data/t_rex.raw.jsonl") as f: data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0] print(f"[raw dataset]: {len(data)} triples, {len(set([i['predicate'] for i in data]))} predicates") data = [i for i in data if filtering(i)] df = pd.DataFrame(data) df = df.drop_duplicates() print(f"[entity only] : {len(df)} triples, {len(df['predicate'].unique())} predicates") count = df.groupby("predicate")['title'].count() df = df[[count[p] >= 3 for p in df['predicate']]] print(f"[remove rare predicate] : {len(df)} triples, {len(df['predicate'].unique())} predicates") with open(f"data/t_rex.filter.jsonl", 'w') as f: for _, i in df.iterrows(): f.write(json.dumps(i.to_dict()) + '\n')