# coding: utf-8 """Script to process Wikipedia and Wikidata dumps and create a knowledge base (KB) with specific parameters. Intermediate files are written to disk. Running the full pipeline on a standard laptop, may take up to 13 hours of processing. Use the -p, -d and -s options to speed up processing using the intermediate files from a previous run. For the Wikidata dump: get the latest-all.json.bz2 from https://dumps.wikimedia.org/wikidatawiki/entities/ For the Wikipedia dump: get enwiki-latest-pages-articles-multistream.xml.bz2 from https://dumps.wikimedia.org/enwiki/latest/ """ from __future__ import unicode_literals import logging from pathlib import Path import plac from bin.wiki_entity_linking import wikipedia_processor as wp, wikidata_processor as wd from bin.wiki_entity_linking import wiki_io as io from bin.wiki_entity_linking import kb_creator from bin.wiki_entity_linking import TRAINING_DATA_FILE, KB_FILE, ENTITY_DESCR_PATH, KB_MODEL_DIR, LOG_FORMAT from bin.wiki_entity_linking import ENTITY_FREQ_PATH, PRIOR_PROB_PATH, ENTITY_DEFS_PATH, ENTITY_ALIAS_PATH import spacy from bin.wiki_entity_linking.kb_creator import read_kb logger = logging.getLogger(__name__) @plac.annotations( wd_json=("Path to the downloaded WikiData JSON dump.", "positional", None, Path), wp_xml=("Path to the downloaded Wikipedia XML dump.", "positional", None, Path), output_dir=("Output directory", "positional", None, Path), model=("Model name or path, should include pretrained vectors.", "positional", None, str), max_per_alias=("Max. # entities per alias (default 10)", "option", "a", int), min_freq=("Min. count of an entity in the corpus (default 20)", "option", "f", int), min_pair=("Min. count of entity-alias pairs (default 5)", "option", "c", int), entity_vector_length=("Length of entity vectors (default 64)", "option", "v", int), loc_prior_prob=("Location to file with prior probabilities", "option", "p", Path), loc_entity_defs=("Location to file with entity definitions", "option", "d", Path), loc_entity_desc=("Location to file with entity descriptions", "option", "s", Path), descr_from_wp=("Flag for using wp descriptions not wd", "flag", "wp"), limit_prior=("Threshold to limit lines read from WP for prior probabilities", "option", "lp", int), limit_train=("Threshold to limit lines read from WP for training set", "option", "lt", int), limit_wd=("Threshold to limit lines read from WD", "option", "lw", int), lang=("Optional language for which to get Wikidata titles. Defaults to 'en'", "option", "la", str), ) def main( wd_json, wp_xml, output_dir, model, max_per_alias=10, min_freq=20, min_pair=5, entity_vector_length=64, loc_prior_prob=None, loc_entity_defs=None, loc_entity_alias=None, loc_entity_desc=None, descr_from_wp=False, limit_prior=None, limit_train=None, limit_wd=None, lang="en", ): entity_defs_path = loc_entity_defs if loc_entity_defs else output_dir / ENTITY_DEFS_PATH entity_alias_path = loc_entity_alias if loc_entity_alias else output_dir / ENTITY_ALIAS_PATH entity_descr_path = loc_entity_desc if loc_entity_desc else output_dir / ENTITY_DESCR_PATH entity_freq_path = output_dir / ENTITY_FREQ_PATH prior_prob_path = loc_prior_prob if loc_prior_prob else output_dir / PRIOR_PROB_PATH training_entities_path = output_dir / TRAINING_DATA_FILE kb_path = output_dir / KB_FILE logger.info("Creating KB with Wikipedia and WikiData") # STEP 0: set up IO if not output_dir.exists(): output_dir.mkdir(parents=True) # STEP 1: Load the NLP object logger.info("STEP 1: Loading NLP model {}".format(model)) nlp = spacy.load(model) # check the length of the nlp vectors if "vectors" not in nlp.meta or not nlp.vocab.vectors.size: raise ValueError( "The `nlp` object should have access to pretrained word vectors, " " cf. https://spacy.io/usage/models#languages." ) # STEP 2: create prior probabilities from WP if not prior_prob_path.exists(): # It takes about 2h to process 1000M lines of Wikipedia XML dump logger.info("STEP 2: Writing prior probabilities to {}".format(prior_prob_path)) if limit_prior is not None: logger.warning("Warning: reading only {} lines of Wikipedia dump".format(limit_prior)) wp.read_prior_probs(wp_xml, prior_prob_path, limit=limit_prior) else: logger.info("STEP 2: Reading prior probabilities from {}".format(prior_prob_path)) # STEP 3: calculate entity frequencies if not entity_freq_path.exists(): logger.info("STEP 3: Calculating and writing entity frequencies to {}".format(entity_freq_path)) io.write_entity_to_count(prior_prob_path, entity_freq_path) else: logger.info("STEP 3: Reading entity frequencies from {}".format(entity_freq_path)) # STEP 4: reading definitions and (possibly) descriptions from WikiData or from file if (not entity_defs_path.exists()) or (not descr_from_wp and not entity_descr_path.exists()): # It takes about 10h to process 55M lines of Wikidata JSON dump logger.info("STEP 4: Parsing and writing Wikidata entity definitions to {}".format(entity_defs_path)) if limit_wd is not None: logger.warning("Warning: reading only {} lines of Wikidata dump".format(limit_wd)) title_to_id, id_to_descr, id_to_alias = wd.read_wikidata_entities_json( wd_json, limit_wd, to_print=False, lang=lang, parse_descr=(not descr_from_wp), ) io.write_title_to_id(entity_defs_path, title_to_id) logger.info("STEP 4b: Writing Wikidata entity aliases to {}".format(entity_alias_path)) io.write_id_to_alias(entity_alias_path, id_to_alias) if not descr_from_wp: logger.info("STEP 4c: Writing Wikidata entity descriptions to {}".format(entity_descr_path)) io.write_id_to_descr(entity_descr_path, id_to_descr) else: logger.info("STEP 4: Reading entity definitions from {}".format(entity_defs_path)) logger.info("STEP 4b: Reading entity aliases from {}".format(entity_alias_path)) if not descr_from_wp: logger.info("STEP 4c: Reading entity descriptions from {}".format(entity_descr_path)) # STEP 5: Getting gold entities from Wikipedia if (not training_entities_path.exists()) or (descr_from_wp and not entity_descr_path.exists()): logger.info("STEP 5: Parsing and writing Wikipedia gold entities to {}".format(training_entities_path)) if limit_train is not None: logger.warning("Warning: reading only {} lines of Wikipedia dump".format(limit_train)) wp.create_training_and_desc(wp_xml, entity_defs_path, entity_descr_path, training_entities_path, descr_from_wp, limit_train) if descr_from_wp: logger.info("STEP 5b: Parsing and writing Wikipedia descriptions to {}".format(entity_descr_path)) else: logger.info("STEP 5: Reading gold entities from {}".format(training_entities_path)) if descr_from_wp: logger.info("STEP 5b: Reading entity descriptions from {}".format(entity_descr_path)) # STEP 6: creating the actual KB # It takes ca. 30 minutes to pretrain the entity embeddings if not kb_path.exists(): logger.info("STEP 6: Creating the KB at {}".format(kb_path)) kb = kb_creator.create_kb( nlp=nlp, max_entities_per_alias=max_per_alias, min_entity_freq=min_freq, min_occ=min_pair, entity_def_path=entity_defs_path, entity_descr_path=entity_descr_path, entity_alias_path=entity_alias_path, entity_freq_path=entity_freq_path, prior_prob_path=prior_prob_path, entity_vector_length=entity_vector_length, ) kb.dump(kb_path) logger.info("kb entities: {}".format(kb.get_size_entities())) logger.info("kb aliases: {}".format(kb.get_size_aliases())) nlp.to_disk(output_dir / KB_MODEL_DIR) else: logger.info("STEP 6: KB already exists at {}".format(kb_path)) logger.info("Done!") if __name__ == "__main__": logging.basicConfig(level=logging.INFO, format=LOG_FORMAT) plac.call(main)