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614 lines
23 KiB
Python
614 lines
23 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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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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# these will/should be matched ignoring case
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wiki_namespaces = ["b", "betawikiversity", "Book", "c", "Category", "Commons",
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"d", "dbdump", "download", "Draft", "Education", "Foundation",
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"Gadget", "Gadget definition", "gerrit", "File", "Help", "Image", "Incubator",
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"m", "mail", "mailarchive", "media", "MediaWiki", "MediaWiki talk", "Mediawikiwiki",
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"MediaZilla", "Meta", "Metawikipedia", "Module",
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"mw", "n", "nost", "oldwikisource", "outreach", "outreachwiki", "otrs", "OTRSwiki",
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"Portal", "phab", "Phabricator", "Project", "q", "quality", "rev",
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"s", "spcom", "Special", "species", "Strategy", "sulutil", "svn",
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"Talk", "Template", "Template talk", "Testwiki", "ticket", "TimedText", "Toollabs", "tools", "tswiki",
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"User", "User talk", "v", "voy",
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"w", "Wikibooks", "Wikidata", "wikiHow", "Wikinvest", "wikilivres", "Wikimedia", "Wikinews",
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"Wikipedia", "Wikipedia talk", "Wikiquote", "Wikisource", "Wikispecies", "Wikitech",
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"Wikiversity", "Wikivoyage", "wikt", "wiktionary", "wmf", "wmania", "WP"]
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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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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_regex(limit=1000)
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title_to_id = _read_wikidata_entities_json(limit=None)
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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. adding", len(entity_list), "entities", datetime.datetime.now())
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print()
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kb.set_entities(entity_list=entity_list, prob_list=entity_frequencies, vector_list=None, feature_list=None)
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# _add_entities(kb, entities=entity_list, probs=entity_frequencies, to_print=to_print)
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print()
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print("4. adding aliases", datetime.datetime.now())
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print()
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_add_aliases(kb, title_to_id=title_to_id, max_entities_per_alias=max_entities_per_alias, min_occ=min_occ,)
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# TODO: read wikipedia texts for entity context
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# _read_wikipedia()
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if to_print:
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print()
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print("kb size:", len(kb), kb.get_size_entities(), kb.get_size_aliases())
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print("done with kb", datetime.datetime.now())
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return kb
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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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while line:
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splits = line.replace('\n', "").split(sep='|')
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# alias = splits[0]
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count = int(splits[1])
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entity = splits[2]
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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 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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def _add_entities_depr(kb, entities, probs, to_print=False):
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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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if to_print:
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print("added", kb.get_size_entities(), "entities:", kb.get_entity_strings())
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def _add_aliases(kb, title_to_id, max_entities_per_alias, min_occ, to_print=False):
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wp_titles = title_to_id.keys()
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if to_print:
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print("wp titles:", wp_titles)
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# adding aliases with prior probabilities
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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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previous_alias = None
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total_count = 0
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counts = list()
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entities = list()
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while line:
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splits = line.replace('\n', "").split(sep='|')
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new_alias = splits[0]
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count = int(splits[1])
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entity = splits[2]
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if new_alias != previous_alias and previous_alias:
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# done reading the previous alias --> output
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if len(entities) > 0:
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selected_entities = list()
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prior_probs = list()
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for ent_count, ent_string in zip(counts, entities):
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if ent_string in wp_titles:
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wd_id = title_to_id[ent_string]
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p_entity_givenalias = ent_count / total_count
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selected_entities.append(wd_id)
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prior_probs.append(p_entity_givenalias)
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if selected_entities:
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try:
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kb.add_alias(alias=previous_alias, entities=selected_entities, probabilities=prior_probs)
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except ValueError as e:
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print(e)
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total_count = 0
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counts = list()
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entities = list()
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total_count += count
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if len(entities) < max_entities_per_alias and count >= min_occ:
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counts.append(count)
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entities.append(entity)
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previous_alias = new_alias
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line = prior_file.readline()
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if to_print:
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print("added", kb.get_size_aliases(), "aliases:", kb.get_alias_strings())
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def _read_wikidata_entities_json(limit=None, to_print=False):
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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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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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parse_sitelinks = True
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parse_labels = False
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parse_descriptions = False
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parse_aliases = False
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with bz2.open(WIKIDATA_JSON, mode='rb') as file:
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line = file.readline()
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cnt = 0
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while line and (not limit or cnt < limit):
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if cnt % 500000 == 0:
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print(datetime.datetime.now(), "processed", cnt, "lines of WikiData dump")
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clean_line = line.strip()
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if clean_line.endswith(b","):
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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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entry_type = obj["type"]
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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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if claim_property:
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for cp in claim_property:
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cp_id = cp['mainsnak'].get('datavalue', {}).get('value', {}).get('id')
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cp_rank = cp['rank']
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if cp_rank != "deprecated" and cp_id in value_set:
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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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# parsing all properties that refer to other entities
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if parse_properties:
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for prop, claim_property in claims.items():
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cp_dicts = [cp['mainsnak']['datavalue'].get('value') for cp in claim_property if cp['mainsnak'].get('datavalue')]
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cp_values = [cp_dict.get('id') for cp_dict in cp_dicts if isinstance(cp_dict, dict) if cp_dict.get('id') is not None]
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if cp_values:
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if to_print:
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print("prop:", prop, cp_values)
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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)
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title_to_id[site] = unique_id
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# print(site, "for", unique_id)
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if parse_labels:
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labels = obj["labels"]
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if labels:
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for lang in languages:
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lang_label = labels.get(lang, None)
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if lang_label:
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if to_print:
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print("label (" + lang + "):", lang_label["value"])
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if parse_descriptions:
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descriptions = obj["descriptions"]
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if descriptions:
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for lang in languages:
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lang_descr = descriptions.get(lang, None)
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if lang_descr:
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if to_print:
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print("description (" + lang + "):", lang_descr["value"])
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if parse_aliases:
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aliases = obj["aliases"]
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if aliases:
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for lang in languages:
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lang_aliases = aliases.get(lang, None)
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if lang_aliases:
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for item in lang_aliases:
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if to_print:
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print("alias (" + lang + "):", item["value"])
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if to_print:
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print()
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line = file.readline()
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cnt += 1
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return title_to_id
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def _read_wikidata_entities_regex_depr(limit=None, to_print=False):
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""" Read the JSON wiki data and parse out the entities with regular expressions. Takes XXX to parse 55M lines. """
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regex_p31 = re.compile(r'mainsnak[^}]*\"P31\"[^}]*}', re.UNICODE)
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regex_id = re.compile(r'\"id\":"Q[0-9]*"', re.UNICODE)
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regex_enwiki = re.compile(r'\"enwiki\":[^}]*}', re.UNICODE)
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regex_title = re.compile(r'\"title\":"[^"]*"', re.UNICODE)
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title_to_id = dict()
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with bz2.open(WIKIDATA_JSON, mode='rb') as file:
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line = file.readline()
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cnt = 0
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while line and (not limit or cnt < limit):
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if cnt % 500000 == 0:
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print(datetime.datetime.now(), "processed", cnt, "lines of WikiData dump")
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clean_line = line.strip()
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if clean_line.endswith(b","):
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clean_line = clean_line[:-1]
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if len(clean_line) > 1:
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clean_line = line.strip().decode("utf-8")
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keep = False
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p31_matches = regex_p31.findall(clean_line)
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if p31_matches:
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for p31_match in p31_matches:
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id_matches = regex_id.findall(p31_match)
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for id_match in id_matches:
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id_match = id_match[6:][:-1]
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if id_match == "Q5" or id_match == "Q15632617":
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keep = True
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if keep:
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id_match = regex_id.search(clean_line).group(0)
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id_match = id_match[6:][:-1]
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enwiki_matches = regex_enwiki.findall(clean_line)
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if enwiki_matches:
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for enwiki_match in enwiki_matches:
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title_match = regex_title.search(enwiki_match).group(0)
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title = title_match[9:][:-1]
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title_to_id[title] = id_match
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# print(title, "for", id_match)
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line = file.readline()
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cnt += 1
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return title_to_id
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def _read_wikipedia_prior_probs():
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""" Read the XML wikipedia data and parse out intra-wiki links to estimate prior probabilities
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The full file takes about 2h to parse 1100M lines (update printed every 5M lines)
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"""
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# find the links
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link_regex = re.compile(r'\[\[[^\[\]]*\]\]')
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# match on interwiki links, e.g. `en:` or `:fr:`
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ns_regex = r":?" + "[a-z][a-z]" + ":"
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# match on Namespace: optionally preceded by a :
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for ns in wiki_namespaces:
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ns_regex += "|" + ":?" + ns + ":"
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ns_regex = re.compile(ns_regex, re.IGNORECASE)
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with bz2.open(ENWIKI_DUMP, mode='rb') as file:
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line = file.readline()
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cnt = 0
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while line:
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if cnt % 5000000 == 0:
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print(datetime.datetime.now(), "processed", cnt, "lines of Wikipedia dump")
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clean_line = line.strip().decode("utf-8")
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matches = link_regex.findall(clean_line)
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for match in matches:
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match = match[2:][:-2].replace("_", " ").strip()
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if ns_regex.match(match):
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pass # ignore namespaces at the beginning of the string
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# this is a simple link, with the alias the same as the mention
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elif "|" not in match:
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_store_alias(match, match, normalize_alias=True, normalize_entity=True)
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# in wiki format, the link is written as [[entity|alias]]
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else:
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splits = match.split("|")
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entity = splits[0].strip()
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alias = splits[1].strip()
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# specific wiki format [[alias (specification)|]]
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if len(alias) == 0 and "(" in entity:
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alias = entity.split("(")[0]
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_store_alias(alias, entity, normalize_alias=False, normalize_entity=True)
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else:
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_store_alias(alias, entity, normalize_alias=False, normalize_entity=True)
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line = file.readline()
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cnt += 1
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# write all aliases and their entities and occurrences to file
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with open(PRIOR_PROB, mode='w', encoding='utf8') as outputfile:
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outputfile.write("alias" + "|" + "count" + "|" + "entity" + "\n")
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for alias, alias_dict in sorted(map_alias_to_link.items(), key=lambda x: x[0]):
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for entity, count in sorted(alias_dict.items(), key=lambda x: x[1], reverse=True):
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outputfile.write(alias + "|" + str(count) + "|" + entity + "\n")
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def _store_alias(alias, entity, normalize_alias=False, normalize_entity=True):
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alias = alias.strip()
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entity = entity.strip()
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# remove everything after # as this is not part of the title but refers to a specific paragraph
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if normalize_entity:
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# wikipedia titles are always capitalized
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entity = capitalize_first(entity.split("#")[0])
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if normalize_alias:
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alias = alias.split("#")[0]
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if alias and entity:
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alias_dict = map_alias_to_link.get(alias, dict())
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entity_count = alias_dict.get(entity, 0)
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alias_dict[entity] = entity_count + 1
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map_alias_to_link[alias] = alias_dict
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def _read_wikipedia():
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""" Read the XML wikipedia data """
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with bz2.open(ENWIKI_DUMP, mode='rb') as file:
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line = file.readline()
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cnt = 1
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article_text = ""
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article_title = None
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article_id = None
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reading_text = False
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while line and cnt < 1000000:
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clean_line = line.strip().decode("utf-8")
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# Start reading new page
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if clean_line == "<page>":
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article_text = ""
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article_title = None
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article_id = 342
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# finished reading this page
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elif clean_line == "</page>":
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if article_id:
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_store_wp_article(article_id, article_title, article_text.strip())
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# start reading text within a page
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if "<text" in clean_line:
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reading_text = True
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if reading_text:
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article_text += " " + clean_line
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# stop reading text within a page
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if "</text" in clean_line:
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reading_text = False
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# read the ID of this article
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ids = re.findall(r"(?<=<id>)\d*(?=</id>)", clean_line)
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if ids:
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article_id = ids[0]
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# read the title of this article
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titles = re.findall(r"(?<=<title>).*(?=</title>)", clean_line)
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if titles:
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article_title = titles[0].strip()
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line = file.readline()
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cnt += 1
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def _store_wp_article(article_id, article_title, article_text):
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pass
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print("WP article", article_id, ":", article_title)
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print(article_text)
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print(_get_clean_wp_text(article_text))
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print()
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|
|
|
|
|
def _get_clean_wp_text(article_text):
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|
# TODO: compile the regular expressions
|
|
|
|
# remove Category and File statements
|
|
clean_text = re.sub(r'\[\[Category:[^\[]*]]', '', article_text)
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|
print("1", clean_text)
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|
clean_text = re.sub(r'\[\[File:[^\[]*]]', '', clean_text) # TODO: this doesn't work yet
|
|
print("2", clean_text)
|
|
|
|
# remove bolding markup
|
|
clean_text = re.sub('\'\'\'', '', clean_text)
|
|
clean_text = re.sub('\'\'', '', clean_text)
|
|
|
|
# remove nested {{info}} statements by removing the inner/smallest ones first and iterating
|
|
try_again = True
|
|
previous_length = len(clean_text)
|
|
while try_again:
|
|
clean_text = re.sub('{[^{]*?}', '', clean_text) # non-greedy match excluding a nested {
|
|
if len(clean_text) < previous_length:
|
|
try_again = True
|
|
else:
|
|
try_again = False
|
|
previous_length = len(clean_text)
|
|
|
|
# remove multiple spaces
|
|
while ' ' in clean_text:
|
|
clean_text = re.sub(' ', ' ', clean_text)
|
|
|
|
# remove simple interwiki links (no alternative name)
|
|
clean_text = re.sub('\[\[([^|]*?)]]', r'\1', clean_text)
|
|
|
|
# remove simple interwiki links by picking the alternative name
|
|
clean_text = re.sub(r'\[\[[^|]*?\|([^|]*?)]]', r'\1', clean_text)
|
|
|
|
# remove HTML comments
|
|
clean_text = re.sub('<!--[^!]*-->', '', clean_text)
|
|
|
|
return clean_text
|
|
|
|
|
|
def add_el(kb, nlp):
|
|
el_pipe = nlp.create_pipe(name='entity_linker', config={"kb": kb})
|
|
nlp.add_pipe(el_pipe, last=True)
|
|
|
|
text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
|
|
"Douglas reminds us to always bring our towel. " \
|
|
"The main character in Doug's novel is the man Arthur Dent, " \
|
|
"but Douglas doesn't write about George Washington or Homer Simpson."
|
|
doc = nlp(text)
|
|
|
|
print()
|
|
for token in doc:
|
|
print("token", token.text, token.ent_type_, token.ent_kb_id_)
|
|
|
|
print()
|
|
for ent in doc.ents:
|
|
print("ent", ent.text, ent.label_, ent.kb_id_)
|
|
|
|
|
|
def capitalize_first(text):
|
|
if not text:
|
|
return None
|
|
result = text[0].capitalize()
|
|
if len(result) > 0:
|
|
result += text[1:]
|
|
return result
|
|
|
|
|
|
if __name__ == "__main__":
|
|
print("START", datetime.datetime.now())
|
|
|
|
to_create_prior_probs = False
|
|
to_create_entity_counts = False
|
|
to_create_kb = True
|
|
to_read_kb = True
|
|
|
|
# STEP 1 : create prior probabilities from WP
|
|
# run only once !
|
|
if to_create_prior_probs:
|
|
print("STEP 1: to_create_prior_probs", datetime.datetime.now())
|
|
_read_wikipedia_prior_probs()
|
|
print()
|
|
|
|
# STEP 2 : deduce entity frequencies from WP
|
|
# run only once !
|
|
if to_create_entity_counts:
|
|
print("STEP 2: to_create_entity_counts", datetime.datetime.now())
|
|
_write_entity_counts()
|
|
print()
|
|
|
|
if to_create_kb:
|
|
# STEP 3 : create KB
|
|
print("STEP 3: to_create_kb", datetime.datetime.now())
|
|
my_nlp = spacy.load('en_core_web_sm')
|
|
my_vocab = my_nlp.vocab
|
|
my_kb = create_kb(my_vocab, max_entities_per_alias=10, min_occ=5, to_print=False)
|
|
print("kb entities:", my_kb.get_size_entities())
|
|
print("kb aliases:", my_kb.get_size_aliases())
|
|
print()
|
|
|
|
# STEP 4 : write KB to file
|
|
print("STEP 4: write KB", datetime.datetime.now())
|
|
my_kb.dump(KB_FILE)
|
|
my_vocab.to_disk(VOCAB_DIR)
|
|
print()
|
|
|
|
if to_read_kb:
|
|
# STEP 5 : read KB back in from file
|
|
print("STEP 5: to_read_kb", datetime.datetime.now())
|
|
my_vocab = Vocab()
|
|
my_vocab.from_disk(VOCAB_DIR)
|
|
my_kb = KnowledgeBase(vocab=my_vocab)
|
|
my_kb.load_bulk(KB_FILE)
|
|
print("kb entities:", my_kb.get_size_entities())
|
|
print("kb aliases:", my_kb.get_size_aliases())
|
|
print()
|
|
|
|
# test KB
|
|
candidates = my_kb.get_candidates("Bush")
|
|
for c in candidates:
|
|
print("entity:", c.entity_)
|
|
print("entity freq:", c.entity_freq)
|
|
print("alias:", c.alias_)
|
|
print("prior prob:", c.prior_prob)
|
|
print()
|
|
|
|
# STEP 6: add KB to NLP pipeline
|
|
# print("STEP 6: use KB", datetime.datetime.now())
|
|
# add_el(my_kb, nlp)
|
|
|
|
print("STOP", datetime.datetime.now())
|