Source code for

# ******************************************************************************
# Copyright 2017-2018 Intel Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************

import os
import logging
import re

from import WikipediaPage
from import \
from import \
from nlp_architect.utils.text import SpacyInstance

os.environ['PYWIKIBOT_NO_USER_CONFIG'] = '1'

DISAMBIGUATE_PAGE = ['wikimedia disambiguation page', 'wikipedia disambiguation page']
NAME_DESCRIPTIONS = ['given name', 'first name', 'family name']

logger = logging.getLogger(__name__)

[docs]class WikiOnline(object): def __init__(self): import pywikibot self.spacy = SpacyInstance() self.pywikibot = pywikibot self.cache = dict() = pywikibot.Site('en', 'wikipedia') # The site we want to run our bot on
[docs] def get_pages(self, phrase): if phrase in self.cache: return self.cache[phrase] ret_pages = set() word_clean = phrase.replace('-', ' ') word_lower = word_clean.lower() word_upper = word_clean.upper() word_title = word_clean.title() words_set = {phrase, word_clean, word_lower, word_upper, word_title} for appr in words_set: try: page_result = self.get_page_redirect(appr) if page_result.pageid != 0: full_page = self.get_wiki_page_with_items(phrase, page_result) ret_pages.add(WikipediaSearchPageResult(appr, full_page)) except Exception as e: logger.error(e) self.cache[phrase] = ret_pages return ret_pages
# pylint: disable=protected-access
[docs] def get_wiki_page_with_items(self, phrase, page): item = self.get_wiki_page_item(page) pageid = page.pageid aliases = self.get_aliases(item) description = self.get_description(item) text = page.text page_title = page._link._title relations = WikipediaPageExtractedRelations() relations.is_disambiguation = self.is_disambiguation_page(item) relations.is_part_name = self.is_name_description(text, item, relations.is_disambiguation) relations.aliases = aliases relations.be_comp, relations.be_comp_norm = self.extract_be_comp(text) relations.extract_relations_from_text_v0(text) ret_page = WikipediaPage(phrase, None, page_title, None, 0, pageid, description, relations) logger.debug('Page: {}. Extracted successfully'.format(ret_page)) return ret_page
[docs] def get_wiki_page_item(self, page): if page is not None: try: item = self.pywikibot.ItemPage.fromPage( page) # this can be used for any page object item.get() # need to call it to access any data. return item except (self.pywikibot.NoPage, AttributeError, TypeError, NameError): pass return None
[docs] def get_page_redirect(self, word): page = self.pywikibot.Page(, word) if page.pageid != 0 and page.isRedirectPage(): return page.getRedirectTarget() return page
[docs] @staticmethod def get_aliases(item): if item is not None and item.aliases is not None: if 'en' in item.aliases: aliases = item.aliases['en'] return aliases return None
[docs] @staticmethod def get_description(item): description = {} if item is not None: item_desc = item.get() if 'desctiptions' in item_desc and 'en' in item_desc['descriptions']: dict([("age", 25)]) description['descriptions'] = dict([('en', item_desc['descriptions']['en'])]) return description
[docs] @staticmethod def is_disambiguation_page(item): if item is not None: dic = item.get() if dic is not None and 'descriptions' in dic: desc = dic['descriptions'] if desc is not None and 'en' in desc: return desc['en'].lower()in DISAMBIGUATE_PAGE return False
[docs] @staticmethod def is_name_description(text, item, is_disambiguation): if item is not None: if is_disambiguation: if WikipediaPageExtractedRelations.is_name_part(text): return True else: dic = item.get() if dic is not None and 'descriptions' in dic: desc = dic['descriptions'] if desc is not None and 'en' in desc: if [s for s in NAME_DESCRIPTIONS if s in desc['en'].lower()]: return True return False
# pylint: disable=no-else-return
[docs] def extract_be_comp(self, text): first_sentence_start_index = text.index("'''") if first_sentence_start_index >= 0: last_temp_index = text.find('\n', first_sentence_start_index) if last_temp_index == -1: last_temp_index = len(text) first_paragraph = text[first_sentence_start_index:last_temp_index] if WikiOnline.extract_be_a_index(first_paragraph) == -1 and last_temp_index != len(text): return self.extract_be_comp(text[last_temp_index:]) elif last_temp_index == len(text): return None, None first_paragraph_clean = re.sub(r'\([^)]*\)', '', first_paragraph) first_paragraph_clean = re.sub(r'<[^>]*>', '', first_paragraph_clean) first_paragraph_clean = re.sub(r'{[^}]*}', '', first_paragraph_clean) first_paragraph_clean = re.sub(r'\[\[[^]]*\]\]', '', first_paragraph_clean) first_paragraph_clean = re.sub(r'[\']', '', first_paragraph_clean) first_paragraph_clean = re.sub(r'&nbsp;', ' ', first_paragraph_clean) return self.extract_be_comp_relations(first_paragraph_clean)
# pylint: disable=not-callable
[docs] def extract_be_comp_relations(self, first_paragraph): be_comp = set() be_comp_norm = set() if first_paragraph: doc = self.spacy.parser(first_paragraph) for token in doc: target = token.text target_lemma = token.lemma_ relation = token.dep_ governor = token.head.text governor_lemma = token.head.lemma_ if relation == 'acl': break if relation == 'punct' and target == '.': break elif relation == 'cop': be_comp.add(governor) be_comp_norm.add(governor_lemma) elif relation == 'nsubj': be_comp.add(target) be_comp_norm.add(target_lemma) elif relation == 'dep': be_comp.add(governor) be_comp_norm.add(governor_lemma) elif relation == 'compound': be_comp.add(target + ' ' + governor) be_comp_norm.add(target_lemma + ' ' + governor_lemma) elif relation == 'amod': be_comp.add(target + ' ' + governor) be_comp_norm.add(target_lemma + ' ' + governor_lemma) elif relation in ['conj', 'appos']: be_comp.add(target) be_comp_norm.add(target_lemma) return be_comp, be_comp_norm
[docs] @staticmethod def extract_be_a_index(sentence): result = None if 'is a' in sentence: result = sentence.index("is a") elif 'are a' in sentence: result = sentence.index("are a") elif 'was a' in sentence: result = sentence.index("was a") elif 'were a' in sentence: result = sentence.index("were a") elif 'be a' in sentence: result = sentence.index("be a") elif 'is the' in sentence: result = sentence.index("is the") elif 'are the' in sentence: result = sentence.index("are the") elif 'was the' in sentence: result = sentence.index("was the") elif 'were the' in sentence: result = sentence.index("were the") elif 'be the' in sentence: result = sentence.index("be the") return result