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calculate entity raw counts offline to speed up KB construction
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svlandeg
parent
19e8f339cb
commit
653b7d9c87
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@ -1,23 +1,25 @@
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# coding: utf-8
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from __future__ import unicode_literals
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from spacy.vocab import Vocab
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"""
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Demonstrate how to build a knowledge base from WikiData and run an Entity Linking algorithm.
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"""
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import re
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import csv
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import json
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import spacy
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import datetime
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import bz2
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from spacy.kb import KnowledgeBase
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from spacy.vocab import Vocab
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# TODO: remove hardcoded paths
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WIKIDATA_JSON = 'C:/Users/Sofie/Documents/data/wikidata/wikidata-20190304-all.json.bz2'
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ENWIKI_DUMP = 'C:/Users/Sofie/Documents/data/wikipedia/enwiki-20190320-pages-articles-multistream.xml.bz2'
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ENWIKI_INDEX = 'C:/Users/Sofie/Documents/data/wikipedia/enwiki-20190320-pages-articles-multistream-index.txt.bz2'
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PRIOR_PROB = 'C:/Users/Sofie/Documents/data/wikipedia/prior_prob.csv'
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ENTITY_COUNTS = 'C:/Users/Sofie/Documents/data/wikipedia/entity_freq.csv'
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KB_FILE = 'C:/Users/Sofie/Documents/data/wikipedia/kb'
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VOCAB_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/vocab'
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@ -44,18 +46,30 @@ map_alias_to_link = dict()
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def create_kb(vocab, max_entities_per_alias, min_occ, to_print=False):
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kb = KnowledgeBase(vocab=vocab)
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id_to_title = _read_wikidata_entities(limit=None)
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title_to_id = {v: k for k, v in id_to_title.items()}
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print()
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print("1. _read_wikidata_entities", datetime.datetime.now())
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print()
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title_to_id = _read_wikidata_entities(limit=100000)
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entity_list = list(id_to_title.keys())
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title_list = [id_to_title[x] for x in entity_list]
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entity_frequencies = _get_entity_frequencies(entities=title_list, to_print=False)
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title_list = list(title_to_id.keys())
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entity_list = [title_to_id[x] for x in title_list]
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print()
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print("2. _get_entity_frequencies", datetime.datetime.now())
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print()
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entity_frequencies = _get_entity_frequencies(entities=title_list)
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print()
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print("3. _add_entities", datetime.datetime.now())
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print()
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_add_entities(kb,
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entities=entity_list,
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probs=entity_frequencies,
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to_print=to_print)
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print()
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print("4. _add_aliases", datetime.datetime.now())
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print()
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_add_aliases(kb,
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title_to_id=title_to_id,
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max_entities_per_alias=max_entities_per_alias,
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@ -72,15 +86,26 @@ def create_kb(vocab, max_entities_per_alias, min_occ, to_print=False):
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return kb
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def _get_entity_frequencies(entities, to_print=False):
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count_entities = [0 for _ in entities]
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def _get_entity_frequencies(entities):
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entity_to_count = dict()
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with open(ENTITY_COUNTS, 'r', encoding='utf8') as csvfile:
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csvreader = csv.reader(csvfile, delimiter='|')
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# skip header
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next(csvreader)
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for row in csvreader:
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entity_to_count[row[0]] = int(row[1])
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return [entity_to_count.get(e, 0) for e in entities]
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def _write_entity_counts(to_print=False):
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entity_to_count = dict()
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total_count = 0
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with open(PRIOR_PROB, mode='r', encoding='utf8') as prior_file:
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# skip header
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prior_file.readline()
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line = prior_file.readline()
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# we can read this file sequentially, it's sorted by alias, and then by count
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while line:
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splits = line.replace('\n', "").split(sep='|')
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@ -88,23 +113,26 @@ def _get_entity_frequencies(entities, to_print=False):
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count = int(splits[1])
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entity = splits[2]
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if entity in entities:
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index = entities.index(entity)
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count_entities[index] = count_entities[index] + count
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current_count = entity_to_count.get(entity, 0)
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entity_to_count[entity] = current_count + count
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total_count += count
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line = prior_file.readline()
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with open(ENTITY_COUNTS, mode='w', encoding='utf8') as entity_file:
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entity_file.write("entity" + "|" + "count" + "\n")
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for entity, count in entity_to_count.items():
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entity_file.write(entity + "|" + str(count) + "\n")
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if to_print:
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for entity, count in zip(entities, count_entities):
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for entity, count in entity_to_count.items():
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print("Entity count:", entity, count)
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print("Total count:", total_count)
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return [x*100 / total_count for x in count_entities]
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def _add_entities(kb, entities, probs, to_print=False):
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# TODO: this should be a bulk method
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for entity, prob in zip(entities, probs):
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kb.add_entity(entity=entity, prob=prob)
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@ -166,13 +194,13 @@ def _add_aliases(kb, title_to_id, max_entities_per_alias, min_occ, to_print=Fals
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def _read_wikidata_entities(limit=None, to_print=False):
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""" Read the JSON wiki data and parse out the entities"""
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""" Read the JSON wiki data and parse out the entities. Takes about 7u30 to parse 55M lines. """
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languages = {'en', 'de'}
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prop_filter = {'P31': {'Q5', 'Q15632617'}} # currently defined as OR: one property suffices to be selected
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site_filter = 'enwiki'
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entity_dict = dict()
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title_to_id = dict()
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# parse appropriate fields - depending on what we need in the KB
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parse_properties = False
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@ -192,12 +220,12 @@ def _read_wikidata_entities(limit=None, to_print=False):
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clean_line = clean_line[:-1]
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if len(clean_line) > 1:
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obj = json.loads(clean_line)
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unique_id = obj["id"]
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entry_type = obj["type"]
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if unique_id[0] == 'Q' and entry_type == "item":
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if entry_type == "item":
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# filtering records on their properties
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keep = False
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claims = obj["claims"]
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for prop, value_set in prop_filter.items():
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claim_property = claims.get(prop, None)
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@ -209,6 +237,8 @@ def _read_wikidata_entities(limit=None, to_print=False):
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keep = True
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if keep:
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unique_id = obj["id"]
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if to_print:
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print("ID:", unique_id)
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print("type:", entry_type)
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@ -225,9 +255,10 @@ def _read_wikidata_entities(limit=None, to_print=False):
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if parse_sitelinks:
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site_value = obj["sitelinks"].get(site_filter, None)
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if site_value:
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site = site_value['title']
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if to_print:
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print(site_filter, ":", site_value['title'])
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entity_dict[unique_id] = site_value['title']
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print(site_filter, ":", site)
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title_to_id[site] = unique_id
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if parse_labels:
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labels = obj["labels"]
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@ -262,7 +293,7 @@ def _read_wikidata_entities(limit=None, to_print=False):
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line = file.readline()
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cnt += 1
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return entity_dict
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return title_to_id
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def _read_wikipedia_prior_probs():
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@ -469,6 +500,7 @@ def capitalize_first(text):
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if __name__ == "__main__":
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to_create_prior_probs = False
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to_create_entity_counts = False
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to_create_kb = True
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to_read_kb = False
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@ -477,20 +509,25 @@ if __name__ == "__main__":
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if to_create_prior_probs:
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_read_wikipedia_prior_probs()
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# STEP 2 : deduce entity frequencies from WP
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# run only once !
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if to_create_entity_counts:
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_write_entity_counts()
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if to_create_kb:
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# STEP 2 : create KB
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# STEP 3 : create KB
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my_nlp = spacy.load('en_core_web_sm')
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my_vocab = my_nlp.vocab
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my_kb = create_kb(my_vocab, max_entities_per_alias=10, min_occ=5, to_print=False)
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print("kb entities:", my_kb.get_size_entities())
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print("kb aliases:", my_kb.get_size_aliases())
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# STEP 3 : write KB to file
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# STEP 4 : write KB to file
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my_kb.dump(KB_FILE)
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my_vocab.to_disk(VOCAB_DIR)
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if to_read_kb:
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# STEP 4 : read KB back in from file
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# STEP 5 : read KB back in from file
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my_vocab = Vocab()
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my_vocab.from_disk(VOCAB_DIR)
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my_kb = KnowledgeBase(vocab=my_vocab)
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@ -507,5 +544,5 @@ if __name__ == "__main__":
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print("alias:", c.alias_)
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print("prior prob:", c.prior_prob)
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# STEP 5: add KB to NLP pipeline
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# STEP 6: add KB to NLP pipeline
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# add_el(my_kb, nlp)
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