{ "metadata": { "name": "DocBookParse" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

Dr. Book - Linking a Book to Open Data Sources

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Abstract:

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For any text-based resource, we will attempt to extract named entities, such as people, places, topics and concepts, to render an annotated version of the text, linking the text to Open Data resources. Our tool parses books authored using the Docbook XML structure (http://docbook.org/tdg51/en/html/) and also uses natural language processing techniques to process raw text, attempting to extract terms which can be linked to wikipedia and other Open Data resources. Our tool is potentially useful for pre and post processing a text document. Pre-processing would take place during the authoring and publication process to create supplementatal resources for inclusion in the text. Post-processing would take place at reading time in the form of a browser or ereader extension which annotates the text of the page.

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Original Idea:

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Two of our team members are involved in the Future of eBooks seminar in which students have created an open-source eBook reader. We believe that books should be platform-independent and plugin-extensible.

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Our team decided to work on a project that would explore the use of Open Data to augment the reading experience by adding semantic web-like links to connect information within a book to the Web.

Project code can be found on github here: https://github.com/AJRenold/doctorbook

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#imports\n", "from bs4 import BeautifulSoup, NavigableString, Tag\n", "import nltk\n", "import pandas as pd\n", "import re\n", "from itertools import islice\n", "from collections import Counter\n", "import os\n", "\n", "# Our tools\n", "from get_wiki_links import WikiUrlFetch, WikiUrlFetchNonDBPedia\n", "from get_wiki_text import Wiki2Plain" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "

TDO: The Discipline of Organizing:

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The team chose Prof. Glushko's upcoming textbook The Discipline of Organizing (TDO) to use to develop ideas for our project. We thought the book would be a good test case for the project, as it is authored in DocBook XML, with structured markup of entities within the text, and many references to entities with wikipedia entries. This allowed us to work on XML and raw text parsing.

" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Read Chapter1.xml into BeautifulSoup" ] }, { "cell_type": "code", "collapsed": false, "input": [ "PATH = 'Chapter1.xml'\n", "soup = BeautifulSoup(open(PATH, 'rt').read())" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Picking up tags:

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TDO uses the DocBook XML markup standard, providing a solid base for analyzing terms by parsing specific tags in the text.

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After exploring the text, we opted to use the tags \"keyword,\" \"author,\" \"indexterm,\" \"orgname,\" \"personname,\" and \"phrase.\"

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Which tags are in the soup?

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "book_tags = set([tag.name for tag in soup.findAll(True)])\n", "print(book_tags)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "set(['ulink', 'surname', 'figure', 'footnote', 'othername', 'primary', 'uri', 'hardware', 'citerefentry', 'manvolnum', 'phrase', 'sidebar', 'tertiary', 'xref', 'sect2', 'para', 'sect1', 'orgname', 'highlights', 'sect1info', 'abbrev', 'filename', 'application', 'emphasis', 'html', 'listitem', 'textobject', 'indexterm', 'chapterinfo', 'firstterm', 'body', 'keywordset', 'blockquote', 'attribution', 'firstname', 'quote', 'symbol', 'literal', 'sect3info', 'citetitle', 'link', 'foreignphrase', 'secondary', 'chapter', 'mediaobject', 'sidebarinfo', 'imagedata', 'keyword', 'glossterm', 'author', 'imageobject', 'personname', 'sect2info', 'refentrytitle', 'action', 'title', 'itemizedlist', 'sect3'])\n" ] } ], "prompt_number": 4 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Using selected tags to construct a Pandas DataFrame with terms from the text" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# The following script looks for terms within select tags and creates a Pandas DataFrame object.\n", "# The DataFrame consists of \"count\" (term counter), \"tag\" (the XML tag for which the term is an attribute),\n", "# and \"term\" (the term found within the tag).\n", "\n", "list_for_df = []\n", "tags = ['keyword','author','indexterm','orgname','personname','phrase']\n", "for tag in tags:\n", " for i in soup.findAll(tag):\n", " if i.string == None:\n", " list_for_df.append({ 'term':\" \".join([ child.lower().encode('utf-8') for child in i.stripped_strings ]), \\\n", " 'tag': tag, 'count': 1 })\n", " else:\n", " list_for_df.append({ 'term': re.sub(' +','', i.string.lower().encode('utf-8')), 'tag': tag, 'count': 1 })\n", " \n", "book_df = pd.DataFrame(list_for_df)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# First 10 entries of the DataFrame.\n", "book_df[0:10]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
counttagterm
0 1 keyword organize
1 1 keyword organizing system
2 1 keyword discipline
3 1 keyword framework
4 1 keyword resource
5 1 keyword collection
6 1 keyword intentional arrangement
7 1 keyword agent
8 1 keyword description resource
9 1 keyword primary resource
\n", "
" ], "output_type": "pyout", "prompt_number": 6, "text": [ " count tag term\n", "0 1 keyword organize\n", "1 1 keyword organizing system\n", "2 1 keyword discipline\n", "3 1 keyword framework\n", "4 1 keyword resource\n", "5 1 keyword collection\n", "6 1 keyword intentional arrangement\n", "7 1 keyword agent\n", "8 1 keyword description resource\n", "9 1 keyword primary resource" ] } ], "prompt_number": 6 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Text Processing with nltk to find Named Entities in the Text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Regex v. Natural Language Processing:

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Our team explored both Regex-based, and Natural Language-based entity recognition techniques. We initially worked with Regex, thinking it would provide a more flexible, rule-based method of identifying terms with less overhead. We quickly realized that Natural Language Processing offers a better solution to our problem. As the old hacker adage goes: don't reinvent the wheel!

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We used NLTK, a Python module rich in natural language tools, to tag words by part of speech. We then focused on parts of speech which could be mapped to named entities, nouns and proper nouns.

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Here's the script for cleaning up the text and tagging each word.

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# We used NLTK (Natural Language Toolkit) to identify terms within the text that are not markedup\n", "# with XML.\n", "\n", "# First we create one large text by stripping the markup from XML.\n", "all_xml = \" \".join([ line for line in soup.stripped_strings])\n", "text = nltk.clean_html(all_xml)\n", "\n", "# We then clean the text through string subsitutions.\n", "text = re.sub(r\"[\\n\\.,\\(\\)\\?\\!\\-':]\",\"\",text)\n", "\n", "# Finally, we use nltk.pos_tag to insert part-of-speech tags to each word in the text.\n", "tagged_text = nltk.pos_tag(text.split(' '))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Noun Chains

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To create a list of potentially named entities in the document, we looked for nouns and variations of nouns (singlar v. plural, general nouns v. proper nouns). We then developed an algorithm to create chains of nouns that appear next to each other. The hypothesis for this is that two or more nouns appearing next to each other likely name the same entity, such as Berkeley California, Nunberg Geoff, or library information systems

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pos = ['NN','NNS','NNP','NNPS'] ## nltk noun tags\n", "\n", "terms = []\n", "chain = []\n", "for term in tagged_text: ## loop through tagged text (in its original order)\n", " \n", " if term[1] in pos: ## if a term is in our pos list, append the term to the chain and to the noun list\n", " chain.append(term)\n", " terms.append(term[0].lower().encode('UTF-8'))\n", " terms.append(\" \".join([ item[0].lower().encode('UTF-8') for item in chain ]))\n", " \n", " else: ## at the end of a sequence of nouns, append the chain to the noun list and reset the chain\n", " if len(chain) > 0:\n", " terms.append(\" \".join([ item[0].lower().encode('UTF-8') for item in chain ]))\n", " chain = []\n", " \n", "terms = [ re.sub(' +',' ',term) for term in terms if term != '' ] # clean extra spaces from noun chains\n", "terms = [ re.sub('^ ','',term) for term in terms ] " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "## sample of terms scrapped\n", "print terms[100:130]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['order', 'order', 'order', '', '', '', 'structure', 'structure', 'organizing', 'structure organizing', 'structure organizing', 'activity', 'activity', 'activity', 'shoes', 'shoes', 'shoes', 'closet', 'closet', 'closet', '', 'books', 'books', 'books', 'book', 'book', 'shelves', 'book shelves', 'book shelves', 'spices']\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Append new terms to DataFrame

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As an approach to processing named entities from a large document, we choose to filter terms that occurred less than a certain number of times. We settled for 2 since setting it higher would have excluded many terms crucial to the understanding of a text. The issue here is that this method, while retaining precision, had very high recall.

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "terms_for_df = []\n", "term_count = Counter(terms)\n", "\n", "# count_threshold should be a number that returns a good amount of information within a reasonable amount of time.\n", "# This depends on what the reader is looking for, and how much computational power they have.\n", "# Theoretically speaking, it could be 0 if a reader wants to see all terms, and can process that amount of data on their computer.\n", "count_threshold = 2\n", "\n", "for term,count in term_count.iteritems():\n", " if count >= count_threshold:\n", " terms_for_df.append({ 'term': term,'count': count })\n", " #print term, count" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# check state of original book_df (366 terms)\n", "book_df" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 11, "text": [ "\n", "Int64Index: 366 entries, 0 to 365\n", "Data columns:\n", "count 366 non-null values\n", "tag 366 non-null values\n", "term 366 non-null values\n", "dtypes: int64(1), object(2)" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "## create list of dict for appending to df\n", "list_for_df = []\n", "for item in terms_for_df:\n", " if item['term'] != \"\" and item['term'] != \" \":\n", " list_for_df.append({ 'term': item['term'], 'count': item['count'], 'tag': 'raw'})" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Append terms scrapped from text to book_df" ] }, { "cell_type": "code", "collapsed": false, "input": [ "book_df = book_df.append(pd.DataFrame(list_for_df))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "## check the raw terms that were appended\n", "df_rawterms = book_df[book_df['tag']=='raw']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "df_rawterms" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 15, "text": [ "\n", "Int64Index: 2863 entries, 0 to 2862\n", "Data columns:\n", "count 2863 non-null values\n", "tag 2863 non-null values\n", "term 2863 non-null values\n", "dtypes: int64(1), object(2)" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# check state of new book_df (3229 terms)\n", "book_df" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 16, "text": [ "\n", "Int64Index: 3229 entries, 0 to 2862\n", "Data columns:\n", "count 3229 non-null values\n", "tag 3229 non-null values\n", "term 3229 non-null values\n", "dtypes: int64(1), object(2)" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "## drop duplicated terms\n", "book_df.drop_duplicates()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 17, "text": [ "\n", "Int64Index: 3190 entries, 0 to 2862\n", "Data columns:\n", "count 3190 non-null values\n", "tag 3190 non-null values\n", "term 3190 non-null values\n", "dtypes: int64(1), object(2)" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Connecting to the web with DBPedia and Wikipedia:

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Our team decided to connect all identified named entities to Wikipedia using a DBpedia Look-up API and DBPedia entity pages. We used an edit distance method to judge the quality of matched entities when the match was not exact. Matches are rated as being \"exact\" (exact match), \"good-partial\" (a likely useful partial match), \"partial\" (dubious match) and \"none\". The match ratings are stored in the DataFrame. After completing the code for this tool, we encapulated the tool in the class WikiUrlFetch, so that it could be easily reusable. This class is available in the appendix of this notebook and on github

\n", "\n", "

This method for matching text terms to Wikipedia entries created two of the main challenges of our project

\n", "
    \n", "
  1. Time dependence on 3rd party APIs - as we worked on mapping named entities to wikipedia urls and expanded our tool from searching for a few terms to searching for thousands of terms, we realized that the response time of the DBPedia APIs was hindering the performance of our tool. For example matching the 3,000+ terms in a single chapter took upwards of three hours.
    2. \n", "
  2. Evaluating match quality - it is a challenge to judge the semantic quality of matches, therefore even good-partial matches do not aways have the same semantic meaning as the term searched
    3. \n", "
\n", "\n", "

*As an attempted solution to the slow response time from DBPedia, we expermented with another Wikipedia Lookup API. This is shown in the class WikiUrlFetchNonDBPedia in the appendix and on Github.

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from get_wiki_links import WikiUrlFetch, WikiUrlFetchNonDBPedia" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 71 }, { "cell_type": "code", "collapsed": false, "input": [ "## example query with WikiUrlFetch\n", "w = WikiUrlFetch()\n", "print w.fetch_wiki(\"Nunberg Geoff\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[{'url': 'http://dbpedia.org/resource/Geoffrey_Nunberg', 'term': 'geoffrey nunberg', 'wiki_url': 'http://en.wikipedia.org/wiki/Geoffrey_Nunberg', 'match': 'good-partial'}]\n" ] } ], "prompt_number": 18 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Using WikiUrlFetch, look up wikipedia URLs for terms in the text" ] }, { "cell_type": "code", "collapsed": true, "input": [ "from itertools import islice\n", "import time\n", "\n", "list_for_wiki_df = []\n", "w = WikiUrlFetch()\n", "\n", "for row in islice(book_df.iterrows(),1000): # iterations limited 100 because WikiUrlFetch because of API limitations\n", "\n", " wikis = w.fetch_wiki(row[1]['term'])\n", " \n", " for wiki in wikis:\n", " print wiki\n", " if wiki['match'] == 'exact' or wiki['match'] == 'good-partial' or wiki['match'] == 'partial':\n", " list_for_wiki_df.append( { 'term': row[1]['term'], 'matched_term': wiki['term'], \\\n", " 'count': row[1]['count'], 'match': wiki['match'], 'url': wiki['wiki_url'] } )" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'url': 'http://dbpedia.org/resource/Secretary', 'term': 'secretary', 'wiki_url': 'http://en.wikipedia.org/wiki/Secretary', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/1994_Winter_Olympics', 'term': '1994 winter olympics', 'wiki_url': 'http://en.wikipedia.org/wiki/1994_Winter_Olympics', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Taboo', 'term': 'taboo', 'wiki_url': 'http://en.wikipedia.org/wiki/Taboo', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Monasticism', 'term': 'monasticism', 'wiki_url': 'http://en.wikipedia.org/wiki/Monasticism', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Assault', 'term': 'assault', 'wiki_url': 'http://en.wikipedia.org/wiki/Assault', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Homicide', 'term': 'homicide', 'wiki_url': 'http://en.wikipedia.org/wiki/Homicide', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Type_(biology)', 'term': 'type (biology)', 'wiki_url': 'http://en.wikipedia.org/wiki/Type_(biology)', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Binomial_nomenclature', 'term': 'binomial nomenclature', 'wiki_url': 'http://en.wikipedia.org/wiki/Binomial_nomenclature', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Metadata', 'term': 'metadata', 'wiki_url': 'http://en.wikipedia.org/wiki/Metadata', 'match': 'exact'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Charles_Darwin', 'term': 'charles darwin', 'wiki_url': 'http://en.wikipedia.org/wiki/Charles_Darwin', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Robert_E._Lee', 'term': 'robert e. lee', 'wiki_url': 'http://en.wikipedia.org/wiki/Robert_E._Lee', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Secretary', 'term': 'secretary', 'wiki_url': 'http://en.wikipedia.org/wiki/Secretary', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/1994_Winter_Olympics', 'term': '1994 winter olympics', 'wiki_url': 'http://en.wikipedia.org/wiki/1994_Winter_Olympics', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Secretary', 'term': 'secretary', 'wiki_url': 'http://en.wikipedia.org/wiki/Secretary', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/1994_Winter_Olympics', 'term': '1994 winter olympics', 'wiki_url': 'http://en.wikipedia.org/wiki/1994_Winter_Olympics', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Greatest_hits_album', 'term': 'greatest hits album', 'wiki_url': 'http://en.wikipedia.org/wiki/Greatest_hits_album', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Toll_road', 'term': 'toll road', 'wiki_url': 'http://en.wikipedia.org/wiki/Toll_road', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Assault', 'term': 'assault', 'wiki_url': 'http://en.wikipedia.org/wiki/Assault', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Homicide', 'term': 'homicide', 'wiki_url': 'http://en.wikipedia.org/wiki/Homicide', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Public_domain', 'term': 'public domain', 'wiki_url': 'http://en.wikipedia.org/wiki/Public_domain', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Contributing_property', 'term': 'contributing property', 'wiki_url': 'http://en.wikipedia.org/wiki/Contributing_property', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Public_domain', 'term': 'public domain', 'wiki_url': 'http://en.wikipedia.org/wiki/Public_domain', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Contributing_property', 'term': 'contributing property', 'wiki_url': 'http://en.wikipedia.org/wiki/Contributing_property', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Extended_play', 'term': 'extended play', 'wiki_url': 'http://en.wikipedia.org/wiki/Extended_play', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Computer', 'term': 'computer', 'wiki_url': 'http://en.wikipedia.org/wiki/Computer', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Terrorism', 'term': 'terrorism', 'wiki_url': 'http://en.wikipedia.org/wiki/Terrorism', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Sport', 'term': 'sport', 'wiki_url': 'http://en.wikipedia.org/wiki/Sport', 'match': 'partial'}\n", "{'term': 'partwhole inclusion', 'match': 'none'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Subset', 'term': 'subset', 'wiki_url': 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'http://dbpedia.org/resource/Trojan_War', 'term': 'trojan war', 'wiki_url': 'http://en.wikipedia.org/wiki/Trojan_War', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Sonar', 'term': 'sonar', 'wiki_url': 'http://en.wikipedia.org/wiki/Sonar', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Alluvium', 'term': 'alluvium', 'wiki_url': 'http://en.wikipedia.org/wiki/Alluvium', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Climate_change', 'term': 'climate change', 'wiki_url': 'http://en.wikipedia.org/wiki/Climate_change', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Global_warming', 'term': 'global warming', 'wiki_url': 'http://en.wikipedia.org/wiki/Global_warming', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/The_Church_of_Jesus_Christ_of_Latter-day_Saints', 'term': 'the church of jesus christ of latter-day saints', 'wiki_url': 'http://en.wikipedia.org/wiki/The_Church_of_Jesus_Christ_of_Latter-day_Saints', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Himalayas', 'term': 'himalayas', 'wiki_url': 'http://en.wikipedia.org/wiki/Himalayas', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Architect', 'term': 'architect', 'wiki_url': 'http://en.wikipedia.org/wiki/Architect', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Medicine', 'term': 'medicine', 'wiki_url': 'http://en.wikipedia.org/wiki/Medicine', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Nuclear_weapon', 'term': 'nuclear weapon', 'wiki_url': 'http://en.wikipedia.org/wiki/Nuclear_weapon', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Torture', 'term': 'torture', 'wiki_url': 'http://en.wikipedia.org/wiki/Torture', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Statistics', 'term': 'statistics', 'wiki_url': 'http://en.wikipedia.org/wiki/Statistics', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Molecule', 'term': 'molecule', 'wiki_url': 'http://en.wikipedia.org/wiki/Molecule', 'match': 'partial'}\n", "{'url': 'http://dbpedia.org/resource/Intellectual', 'term': 'intellectual', 'wiki_url': 'http://en.wikipedia.org/wiki/Intellectual', 'match': 'exact'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Scientific_journal', 'term': 'scientific journal', 'wiki_url': 'http://en.wikipedia.org/wiki/Scientific_journal', 'match': 'partial'}" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "{'url': 'http://dbpedia.org/resource/Public_speaking', 'term': 'public speaking', 'wiki_url': 'http://en.wikipedia.org/wiki/Public_speaking', 'match': 'partial'}\n" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "print len(list_for_wiki_df)\n", "new_cols_df = pd.DataFrame(list_for_wiki_df)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1154\n" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "## extract the exact matches\n", "exact_match = new_cols_df[new_cols_df['match'] == 'exact']\n", "exact_match[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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countmatchmatched_termtermurl
8 1 exact metadata metadata http://en.wikipedia.org/wiki/Metadata
60 1 exact cataloging cataloging http://en.wikipedia.org/wiki/Cataloging
108 1 exact adam smith smith, adam http://en.wikipedia.org/wiki/Adam_Smith
109 1 exact charles darwin darwin, charles http://en.wikipedia.org/wiki/Charles_Darwin
116 1 exact cern cern http://en.wikipedia.org/wiki/CERN
151 1 exact collocation collocation http://en.wikipedia.org/wiki/Collocation
178 1 exact ifla ifla http://en.wikipedia.org/wiki/IFLA
309 1 exact cataloging cataloging http://en.wikipedia.org/wiki/Cataloging
312 1 exact ronald coase coase, ronald http://en.wikipedia.org/wiki/Ronald_Coase
313 1 exact adam smith smith, adam http://en.wikipedia.org/wiki/Adam_Smith
\n", "
" ], "output_type": "pyout", "prompt_number": 21, "text": [ " count match matched_term term \\\n", "8 1 exact metadata metadata \n", "60 1 exact cataloging cataloging \n", "108 1 exact adam smith smith, adam \n", "109 1 exact charles darwin darwin, charles \n", "116 1 exact cern cern \n", "151 1 exact collocation collocation \n", "178 1 exact ifla ifla \n", "309 1 exact cataloging cataloging \n", "312 1 exact ronald coase coase, ronald \n", "313 1 exact adam smith smith, adam \n", "\n", " url \n", "8 http://en.wikipedia.org/wiki/Metadata \n", "60 http://en.wikipedia.org/wiki/Cataloging \n", "108 http://en.wikipedia.org/wiki/Adam_Smith \n", "109 http://en.wikipedia.org/wiki/Charles_Darwin \n", "116 http://en.wikipedia.org/wiki/CERN \n", "151 http://en.wikipedia.org/wiki/Collocation \n", "178 http://en.wikipedia.org/wiki/IFLA \n", "309 http://en.wikipedia.org/wiki/Cataloging \n", "312 http://en.wikipedia.org/wiki/Ronald_Coase \n", "313 http://en.wikipedia.org/wiki/Adam_Smith " ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "## extract the good matches\n", "good_match = new_cols_df[new_cols_df['match'] == 'good-partial']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "## extract the partial matches\n", "partial_match = new_cols_df[new_cols_df['match'] == 'partial']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "## Create a DataFrame with only good-partial and exact match terms\n", "matched_terms = good_match.append(exact_match)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "matched_terms" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 26, "text": [ "\n", "Int64Index: 158 entries, 33 to 1151\n", "Data columns:\n", "count 158 non-null values\n", "match 158 non-null values\n", "matched_term 158 non-null values\n", "term 158 non-null values\n", "url 158 non-null values\n", "dtypes: int64(1), object(4)" ] } ], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "## first 10 matched_terms\n", "matched_terms[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
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countmatchmatched_termtermurl
33 1 good-partial geoffrey nunberg nunberg, geoff http://en.wikipedia.org/wiki/Geoffrey_Nunberg
73 1 good-partial rai uri http://en.wikipedia.org/wiki/RAI
76 1 good-partial rai uri http://en.wikipedia.org/wiki/RAI
99 1 good-partial data set dataset http://en.wikipedia.org/wiki/Data_set
243 1 good-partial sergey brin brin, sergei http://en.wikipedia.org/wiki/Sergey_Brin
252 1 good-partial paul samuelson samuelson, pamela http://en.wikipedia.org/wiki/Paul_Samuelson
259 1 good-partial hathitrust hathi trust http://en.wikipedia.org/wiki/HathiTrust
266 1 good-partial tulku sku http://en.wikipedia.org/wiki/Tulku
306 1 good-partial tulku sku http://en.wikipedia.org/wiki/Tulku
320 1 good-partial moore's law moore's law http://en.wikipedia.org/wiki/Moore%27s_law
\n", "
" ], "output_type": "pyout", "prompt_number": 25, "text": [ " count match matched_term term \\\n", "33 1 good-partial geoffrey nunberg nunberg, geoff \n", "73 1 good-partial rai uri \n", "76 1 good-partial rai uri \n", "99 1 good-partial data set dataset \n", "243 1 good-partial sergey brin brin, sergei \n", "252 1 good-partial paul samuelson samuelson, pamela \n", "259 1 good-partial hathitrust hathi trust \n", "266 1 good-partial tulku sku \n", "306 1 good-partial tulku sku \n", "320 1 good-partial moore's law moore's law \n", "\n", " url \n", "33 http://en.wikipedia.org/wiki/Geoffrey_Nunberg \n", "73 http://en.wikipedia.org/wiki/RAI \n", "76 http://en.wikipedia.org/wiki/RAI \n", "99 http://en.wikipedia.org/wiki/Data_set \n", "243 http://en.wikipedia.org/wiki/Sergey_Brin \n", "252 http://en.wikipedia.org/wiki/Paul_Samuelson \n", "259 http://en.wikipedia.org/wiki/HathiTrust \n", "266 http://en.wikipedia.org/wiki/Tulku \n", "306 http://en.wikipedia.org/wiki/Tulku \n", "320 http://en.wikipedia.org/wiki/Moore%27s_law " ] } ], "prompt_number": 25 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "After we get the Wikipedia URL, we use Wiki2Plain to extract the page summary and image for the matched_terms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

We chose \"exact\" and \"good-partial\" matches for Wikipedia linking. Many of the terms returned correct results (see results below), while some returned incorrect pages, e.g. Radio Televisione Italiana for URI. This shows the challenge of finding good results when looking up Wikipedia URLs. The class Wiki2Plain, allows us to scrape the summary and image from a Wikipedia page. The class is available in the Appendix and on Github.

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## function using Wiki2Plain results to create two new DataFrame columns using apply\n", "def get_wiki_textimage(url):\n", " print url\n", " try:\n", " wiki = Wiki2Plain(url)\n", " except:\n", " return \"none\",\"none\"\n", " return wiki.text, wiki.image()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "code", "collapsed": true, "input": [ "## apply and use tuple result to create two DataFrames\n", "## from http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/#create\n", "\n", "matched_terms['wiki_text'],matched_terms['wiki_image'] = zip(*matched_terms['url'].apply(get_wiki_textimage))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "http://en.wikipedia.org/wiki/Geoffrey_Nunberg\n", "http://en.wikipedia.org/wiki/RAI" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/RAI" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Data_set" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Sergey_Brin" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Paul_Samuelson" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/HathiTrust" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Tulku" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Tulku" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Moore%27s_law" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Geoffrey_Nunberg" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Tim_O'Reilly" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Yahoo!" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Yahoo!" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/DVD" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Presenter" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Clothing" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Interdependence" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Museum" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Pula" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Concept_car" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Separatism" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Home_page" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Sidebar" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Implementation" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/MP3" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Algorithm" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Exhibition" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Writer" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/History" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/School" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Merchant" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Camera" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Stratum" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Grape" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Sensor" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Cookbook" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Neighbourhood" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Consistency" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Operating_system" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Enabling" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/LIFO" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Gas" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/PAL" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Heuristic" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Solution" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Textbook" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Encyclopedia" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Moore's_law" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/NPR" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Psychologist" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Analogy" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Telecommunication" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/V8_engine" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Internet" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Speech" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Soap" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Employment" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Hangar" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Image" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Utilitarianism" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Temple" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Europe" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Bicycle" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Cycling" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Yahoo!" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Copyright" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Species" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Zoo" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Metamodeling" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Paragraph" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/S._Harinarayana" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Count" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Sherd" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Metadata" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Cataloging" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Adam_Smith" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Charles_Darwin" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/CERN" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Collocation" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/IFLA" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Cataloging" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Ronald_Coase" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Adam_Smith" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/EXIF" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/EBay" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Twitter" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Facebook" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Folksonomy" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Flickr" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Google" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Google" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/SeaWorld" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Google" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Google" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Google" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Flickr" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Westlaw" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/LexisNexis" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Google" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/EBay" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Wikipedia" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Facebook" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Twitter" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/YouTube" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Google" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Microsoft" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Google" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Microsoft" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Herman_Melville" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Adam_Smith" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Charles_Darwin" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Computer_science" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Cooking" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Metadata" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Granularity" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Economics" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Law" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Castle" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Wine" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Synonyms" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Windshield" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Coffee" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Aisle" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Historian" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Fiction" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Geneva" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Tool" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/EBay" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Iron" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Bridge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Fashion" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/IBM" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Chess" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Phonograph" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Anatomy" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Reimbursement" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Grass" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Nobel_Prize" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Keywords" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Architecture" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Bookmarking" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Kitchen" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Papyrus" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Logic" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Negotiation" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Website" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Publisher" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Physician" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Complexity" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Business" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Human" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Instantiation" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Judge" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Standardization" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Manufacturing" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Cemetery" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "http://en.wikipedia.org/wiki/Intellectual" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "## first ten terms\n", "matched_terms[:10]" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmatchmatched_termtermurlwiki_textwiki_imageflickr_urls
33 1 good-partial geoffrey nunberg nunberg, geoff http://en.wikipedia.org/wiki/Geoffrey_Nunberg Geoffrey Nunberg (born June, 1945) is an Ameri... None [http://farm9.staticflickr.com/8140/8700510731...
73 1 good-partial rai uri http://en.wikipedia.org/wiki/RAI RAI — Radiotelevisione Italiana S.p.A. (... None [http://farm9.staticflickr.com/8269/8699910887...
76 1 good-partial rai uri http://en.wikipedia.org/wiki/RAI RAI — Radiotelevisione Italiana S.p.A. (... None [http://farm9.staticflickr.com/8269/8699910887...
99 1 good-partial data set dataset http://en.wikipedia.org/wiki/Data_set A dataset (or data set) is a collection of dat... None [http://farm9.staticflickr.com/8527/8590445473...
243 1 good-partial sergey brin brin, sergei http://en.wikipedia.org/wiki/Sergey_Brin Sergey Mikhaylovich Brin (\u0421\u0435\u0440\u0433\u0435\u0439 \u041c\u0438\u0445\u0430\u0439\u043b\u043e\u0432\u0438\u0447 \u0411\u0440... http://simple.wikipedia.org/w/index.php?title=... [http://farm9.staticflickr.com/8407/8699842532...
252 1 good-partial paul samuelson samuelson, pamela http://en.wikipedia.org/wiki/Paul_Samuelson Paul Anthony Samuelson (May 15, 1915 \u2013 Decembe... http://simple.wikipedia.org/w/index.php?title=... [http://farm9.staticflickr.com/8280/8700678957...
259 1 good-partial hathitrust hathi trust http://en.wikipedia.org/wiki/HathiTrust HathiTrust is a large-scale collaborative repo... None [http://farm8.staticflickr.com/7088/7178341310...
266 1 good-partial tulku sku http://en.wikipedia.org/wiki/Tulku In Tibetan Buddhism, \"tulku\" (, also t\u00fclku, tr... None [http://farm9.staticflickr.com/8113/8698495870...
306 1 good-partial tulku sku http://en.wikipedia.org/wiki/Tulku In Tibetan Buddhism, \"tulku\" (, also t\u00fclku, tr... None [http://farm9.staticflickr.com/8113/8698495870...
320 1 good-partial moore's law moore's law http://en.wikipedia.org/wiki/Moore%27s_law none none [http://farm9.staticflickr.com/8230/8597007467...
\n", "
" ], "output_type": "pyout", "prompt_number": 94, "text": [ " count match matched_term term \\\n", "33 1 good-partial geoffrey nunberg nunberg, geoff \n", "73 1 good-partial rai uri \n", "76 1 good-partial rai uri \n", "99 1 good-partial data set dataset \n", "243 1 good-partial sergey brin brin, sergei \n", "252 1 good-partial paul samuelson samuelson, pamela \n", "259 1 good-partial hathitrust hathi trust \n", "266 1 good-partial tulku sku \n", "306 1 good-partial tulku sku \n", "320 1 good-partial moore's law moore's law \n", "\n", " url \\\n", "33 http://en.wikipedia.org/wiki/Geoffrey_Nunberg \n", "73 http://en.wikipedia.org/wiki/RAI \n", "76 http://en.wikipedia.org/wiki/RAI \n", "99 http://en.wikipedia.org/wiki/Data_set \n", "243 http://en.wikipedia.org/wiki/Sergey_Brin \n", "252 http://en.wikipedia.org/wiki/Paul_Samuelson \n", "259 http://en.wikipedia.org/wiki/HathiTrust \n", "266 http://en.wikipedia.org/wiki/Tulku \n", "306 http://en.wikipedia.org/wiki/Tulku \n", "320 http://en.wikipedia.org/wiki/Moore%27s_law \n", "\n", " wiki_text \\\n", "33 Geoffrey Nunberg (born June, 1945) is an Ameri... \n", "73 RAI — Radiotelevisione Italiana S.p.A. (... \n", "76 RAI — Radiotelevisione Italiana S.p.A. (... \n", "99 A dataset (or data set) is a collection of dat... \n", "243 Sergey Mikhaylovich Brin (\u0421\u0435\u0440\u0433\u0435\u0439 \u041c\u0438\u0445\u0430\u0439\u043b\u043e\u0432\u0438\u0447 \u0411\u0440... \n", "252 Paul Anthony Samuelson (May 15, 1915 \u2013 Decembe... \n", "259 HathiTrust is a large-scale collaborative repo... \n", "266 In Tibetan Buddhism, \"tulku\" (, also t\u00fclku, tr... \n", "306 In Tibetan Buddhism, \"tulku\" (, also t\u00fclku, tr... \n", "320 none \n", "\n", " wiki_image \\\n", "33 None \n", "73 None \n", "76 None \n", "99 None \n", "243 http://simple.wikipedia.org/w/index.php?title=... \n", "252 http://simple.wikipedia.org/w/index.php?title=... \n", "259 None \n", "266 None \n", "306 None \n", "320 none \n", "\n", " flickr_urls \n", "33 [http://farm9.staticflickr.com/8140/8700510731... \n", "73 [http://farm9.staticflickr.com/8269/8699910887... \n", "76 [http://farm9.staticflickr.com/8269/8699910887... \n", "99 [http://farm9.staticflickr.com/8527/8590445473... \n", "243 [http://farm9.staticflickr.com/8407/8699842532... \n", "252 [http://farm9.staticflickr.com/8280/8700678957... \n", "259 [http://farm8.staticflickr.com/7088/7178341310... \n", "266 [http://farm9.staticflickr.com/8113/8698495870... \n", "306 [http://farm9.staticflickr.com/8113/8698495870... \n", "320 [http://farm9.staticflickr.com/8230/8597007467... " ] } ], "prompt_number": 94 }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Our result for Adam Smith (DataFram index 158)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# http://ipython.org/ipython-doc/dev/api/generated/IPython.core.display.html#IPython.core.display.Image\n", "# width is IPython 14+, this is IPython version 0.13.1\n", "from IPython.core.display import Image\n", "print matched_terms.ix[243]['wiki_text']\n", "Image(url=matched_terms.ix[243]['wiki_image'])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Sergey Mikhaylovich Brin (\u0421\u0435\u0440\u0433\u0435\u0439 \u041c\u0438\u0445\u0430\u0439\u043b\u043e\u0432\u0438\u0447 \u0411\u0440\u0438\u043d) (born August 21, 1973) is an American computer scientist and Internet entrepreneur who, with Larry Page, co-founded Google, one of the most profitable Internet companies. , his personal wealth is estimated to be $20.3 billion. Together, Brin and Page own about 16 percent of the company.\n", "\n", "Brin immigrated to the United States with his family from the Soviet Union at the age of six. He earned his undergraduate degree at the University of Maryland, following in his father's and grandfather's footsteps by studying mathematics, as well as computer science. After graduation, he moved to Stanford University to acquire a Ph.D. in computer science. There he met Larry Page, with whom he later became friends. They crammed their dormitory room with inexpensive computers and applied Brin's data mining system to build a superior search engine. The program became popular at Stanford and they suspended their PhD studies to start up Google in a rented garage.\n", "\n", "The Economist newspaper referred to Brin as an \"Enlightenment Man\", and someone who believes that \"knowledge is always good, and certainly always better than ignorance\", a philosophy that is summed up by Google\u2019s motto \"Organize the world\u2019s information and make it universally accessible and useful\" and \"Don't be evil\".\n", "\n", "\n" ] }, { "html": [ "" ], "output_type": "pyout", "prompt_number": 70, "text": [ "" ] } ], "prompt_number": 70 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Other APIs - Flickr and News (Guardian)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Our project also explored the use of Flickr and News APIs that run queries based on the terms matched from Wikipedia. These are potentially useful in various ways.

" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## load imports and API Keys\n", "from flickr_api.auth import AuthHandler\n", "from flickr_api import FlickrError\n", "import flickr_api\n", "from tidings import Guardian, NewYorkTimes\n", "\n", "def load_api_keys():\n", " if os.path.exists('settings.py'):\n", " from settings import FLICKR_KEY, FLICKR_SECRET, guardian_key, nyt_key\n", " secrets = {'api_key': FLICKR_KEY, 'api_secret': FLICKR_SECRET}\n", " news_keys = {'guardian_key': guardian_key, 'nyt_key': nyt_key }\n", " return secrets, news_keys\n", " else:\n", " print(\"settings.py not found, add to directory or run without flickr and news\")\n", " sys.exit()\n", "\n", "secrets, news_keys = load_api_keys()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 105 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Flickr" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_flickr_url(term):\n", " flickr_api.set_keys(**secrets)\n", " photos = flickr_api.Photo.search(tags=term, sort='date-posted-desc')\n", " \n", " if len(photos) == 0 or type(photos) == dict:\n", " return []\n", " \n", " flickr_urls = []\n", " for photo in photos[:3]:\n", " try:\n", " flickr_urls.append(\"http://farm{farm}.staticflickr.com/{server}/{id}_{secret}_m.jpg\".format(**photo.getInfo()))\n", " except FlickrError:\n", " pass\n", "\n", " return flickr_urls\n", "\n", "matched_terms['flickr_urls'] = matched_terms['term'].apply(get_flickr_url)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 72 }, { "cell_type": "code", "collapsed": false, "input": [ "## which flickr images did we get for the term 'museums'?\n", "print matched_terms.ix[565]['term']\n", "flickr_url = matched_terms.ix[565]['flickr_urls'][1]\n", "Image(url=flickr_url)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "museums\n" ] }, { "html": [ "" ], "output_type": "pyout", "prompt_number": 99, "text": [ "" ] } ], "prompt_number": 99 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Guardian UK" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## blocked from API?\n", "def get_news_url(term):\n", " g = Guardian(news_keys['guardian_key'])\n", " print term\n", " g_links = g.query(re.sub(\"\\W\",\"\",term), from_date='2013-01-01', to_date='2013-4-30')\n", " return g_links[:3]\n", "\n", "matched_terms['news_urls'], = matched_terms['term'].apply(get_news_url)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "HTTPError", "evalue": "403 Client Error: Forbidden", "output_type": "pyerr", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mg_links\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mmatched_terms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'news_urls'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatched_terms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'term'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_news_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.10.1-py2.7-macosx-10.8-intel.egg/pandas/core/series.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m 2294\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2298\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Library/Python/2.7/site-packages/pandas-0.10.1-py2.7-macosx-10.8-intel.egg/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.map_infer (pandas/lib.c:39944)\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m\u001b[0m in \u001b[0;36mget_news_url\u001b[0;34m(term)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGuardian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnews_keys\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'guardian_key'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mterm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mg_links\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\W\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mterm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_date\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2013-01-01'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_date\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'2013-4-30'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mg_links\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/ajrenold/Dropbox/iSchool/2013Spring/OpenData/doctorbook/doctorbook/notebooks/tidings.pyc\u001b[0m in \u001b[0;36mquery\u001b[0;34m(self, query, from_date, to_date)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_date\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoday\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%Y-01-01'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m 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countmatchmatched_termtermurlwiki_textwiki_imageflickr_urls
33 1 good-partial geoffrey nunberg nunberg, geoff http://en.wikipedia.org/wiki/Geoffrey_Nunberg Geoffrey Nunberg (born June, 1945) is an Ameri... None [http://farm9.staticflickr.com/8140/8700510731...
73 1 good-partial rai uri http://en.wikipedia.org/wiki/RAI RAI — Radiotelevisione Italiana S.p.A. (... None [http://farm9.staticflickr.com/8269/8699910887...
76 1 good-partial rai uri http://en.wikipedia.org/wiki/RAI RAI — Radiotelevisione Italiana S.p.A. (... None [http://farm9.staticflickr.com/8269/8699910887...
99 1 good-partial data set dataset http://en.wikipedia.org/wiki/Data_set A dataset (or data set) is a collection of dat... None [http://farm9.staticflickr.com/8527/8590445473...
243 1 good-partial sergey brin brin, sergei http://en.wikipedia.org/wiki/Sergey_Brin Sergey Mikhaylovich Brin (\u0421\u0435\u0440\u0433\u0435\u0439 \u041c\u0438\u0445\u0430\u0439\u043b\u043e\u0432\u0438\u0447 \u0411\u0440... http://simple.wikipedia.org/w/index.php?title=... [http://farm9.staticflickr.com/8407/8699842532...
252 1 good-partial paul samuelson samuelson, pamela http://en.wikipedia.org/wiki/Paul_Samuelson Paul Anthony Samuelson (May 15, 1915 \u2013 Decembe... http://simple.wikipedia.org/w/index.php?title=... [http://farm9.staticflickr.com/8280/8700678957...
259 1 good-partial hathitrust hathi trust http://en.wikipedia.org/wiki/HathiTrust HathiTrust is a large-scale collaborative repo... None [http://farm8.staticflickr.com/7088/7178341310...
266 1 good-partial tulku sku http://en.wikipedia.org/wiki/Tulku In Tibetan Buddhism, \"tulku\" (, also t\u00fclku, tr... None [http://farm9.staticflickr.com/8113/8698495870...
306 1 good-partial tulku sku http://en.wikipedia.org/wiki/Tulku In Tibetan Buddhism, \"tulku\" (, also t\u00fclku, tr... None [http://farm9.staticflickr.com/8113/8698495870...
320 1 good-partial moore's law moore's law http://en.wikipedia.org/wiki/Moore%27s_law none none [http://farm9.staticflickr.com/8230/8597007467...
\n", "
" ], "output_type": "pyout", "prompt_number": 115, "text": [ " count match matched_term term \\\n", "33 1 good-partial geoffrey nunberg nunberg, geoff \n", "73 1 good-partial rai uri \n", "76 1 good-partial rai uri \n", "99 1 good-partial data set dataset \n", "243 1 good-partial sergey brin brin, sergei \n", "252 1 good-partial paul samuelson samuelson, pamela \n", "259 1 good-partial hathitrust hathi trust \n", "266 1 good-partial tulku sku \n", "306 1 good-partial tulku sku \n", "320 1 good-partial moore's law moore's law \n", "\n", " url \\\n", "33 http://en.wikipedia.org/wiki/Geoffrey_Nunberg \n", "73 http://en.wikipedia.org/wiki/RAI \n", "76 http://en.wikipedia.org/wiki/RAI \n", "99 http://en.wikipedia.org/wiki/Data_set \n", "243 http://en.wikipedia.org/wiki/Sergey_Brin \n", "252 http://en.wikipedia.org/wiki/Paul_Samuelson \n", "259 http://en.wikipedia.org/wiki/HathiTrust \n", "266 http://en.wikipedia.org/wiki/Tulku \n", "306 http://en.wikipedia.org/wiki/Tulku \n", "320 http://en.wikipedia.org/wiki/Moore%27s_law \n", "\n", " wiki_text \\\n", "33 Geoffrey Nunberg (born June, 1945) is an Ameri... \n", "73 RAI — Radiotelevisione Italiana S.p.A. (... \n", "76 RAI — Radiotelevisione Italiana S.p.A. (... \n", "99 A dataset (or data set) is a collection of dat... \n", "243 Sergey Mikhaylovich Brin (\u0421\u0435\u0440\u0433\u0435\u0439 \u041c\u0438\u0445\u0430\u0439\u043b\u043e\u0432\u0438\u0447 \u0411\u0440... \n", "252 Paul Anthony Samuelson (May 15, 1915 \u2013 Decembe... \n", "259 HathiTrust is a large-scale collaborative repo... \n", "266 In Tibetan Buddhism, \"tulku\" (, also t\u00fclku, tr... \n", "306 In Tibetan Buddhism, \"tulku\" (, also t\u00fclku, tr... \n", "320 none \n", "\n", " wiki_image \\\n", "33 None \n", "73 None \n", "76 None \n", "99 None \n", "243 http://simple.wikipedia.org/w/index.php?title=... \n", "252 http://simple.wikipedia.org/w/index.php?title=... \n", "259 None \n", "266 None \n", "306 None \n", "320 none \n", "\n", " flickr_urls \n", "33 [http://farm9.staticflickr.com/8140/8700510731... \n", "73 [http://farm9.staticflickr.com/8269/8699910887... \n", "76 [http://farm9.staticflickr.com/8269/8699910887... \n", "99 [http://farm9.staticflickr.com/8527/8590445473... \n", "243 [http://farm9.staticflickr.com/8407/8699842532... \n", "252 [http://farm9.staticflickr.com/8280/8700678957... \n", "259 [http://farm8.staticflickr.com/7088/7178341310... \n", "266 [http://farm9.staticflickr.com/8113/8698495870... \n", "306 [http://farm9.staticflickr.com/8113/8698495870... \n", "320 [http://farm9.staticflickr.com/8230/8597007467... " ] } ], "prompt_number": 115 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Future Project Direction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

As a potentially useful tool for augmenting the reading experience with Open Data. Our group envisions two potential directions for our project or similar

\n", "
    \n", "
  1. Pre-publishing Processing - where a non-fiction book in its raw form (such as DocBook XML) goes through a process where Open Data sources, such as a Wikipedia links, are inserted into the mark up. This step would take hours to run for an entire book. This would need improvement in the match quality algorithm and possibly human quality assurance of the inserted links.
    2. \n", "
  2. Reading-time Ereader Extension - where our tool (shown in the prototype) could be built as an extension to an open source ereader, such as the product under development by FuturePress. The extension would annotate any document a user is reading at reading-time with annotations linking entities to their Wikipedia pages. For this use case our matching algorithm would need a lot of improvement!
    3. \n", "
" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Appendix" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Note on Python String Encoding" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

From http://farmdev.com/talks/unicode/

\n", "\n", "

When retrieving text from web resources and APIs and then storing the text, it is important to strictly convert strings to unicode. This will help to handle non-ascii characters before unicode only charaters start to create mystical bugs in the application. Areas where we ran into string encoding problems were attempting to store an DataFrame as a csv file and doing string comparisons (Can't compare a non-unicode string to a unicode string!). \n", "\n" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Save/Open Data Frame" ] }, { "cell_type": "code", "collapsed": false, "input": [ "## write DataFrame\n", "import os\n", "path = os.getcwd() + '/wiki_urls_saved.csv'\n", "encoding = 'UTF-8'\n", "sep = ','\n", "matched_terms.to_csv(path,sep,encoding)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 73 }, { "cell_type": "code", "collapsed": false, "input": [ "## read DataFrame\n", "new_df = pd.read_csv(path)\n", "new_df = new_df.drop(['Unnamed: 0'],axis=1)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "WikiUrlFetch Class (two working versions WikiUrlFetch and WikiUrlFetchNonDBPedia)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#!/usr/bin/env python\n", "\n", "from urllib2 import urlopen\n", "import urllib2\n", "import re\n", "import json\n", "from nltk import metrics\n", "from bs4 import BeautifulSoup, Tag\n", "\n", "class WikiUrlFetch():\n", "\n", " def __init__(self):\n", " self.cleaned_term = None\n", "\n", " def fetch_wiki(self,term):\n", " self.cleaned_term = self.clean_term(term)\n", " self.results = self.get_wiki_url(self.cleaned_term)\n", " return self.results\n", "\n", " def clean_term(self,term):\n", " return re.sub(r\"[^A-Za-z0-9 ]\",\"\",term.lower())\n", " \n", " def check_dbpedia(self, term):\n", " api = 'http://lookup.dbpedia.org/api/search.asmx/KeywordSearch?MaxHits=5&QueryString='\n", " #api = 'http://lookup.dbpedia.org/api/search.asmx/PrefixSearch?MaxHits=10&QueryString='\n", " \n", " #print term\n", "\n", " try:\n", " response = urlopen(api+term)\n", " except:\n", " return \"\"\n", " \n", " soup = BeautifulSoup(response.read())\n", " \n", " results = []\n", " for result in soup.findAll('result'):\n", " for child in result.children:\n", " if isinstance(child,Tag):\n", " if child.name == 'label':\n", " current_label = child.string.lower()\n", " if child.name == 'uri':\n", " results.append({ 'term': current_label.encode('utf-8'), 'url': child.string.encode('utf-8') })\n", " \n", " return self.rank_dbpedia_results(results,term)\n", " \n", " def normalize(self,string):\n", " strings = string.split(\" \")\n", " strings.sort()\n", " return \" \".join(strings)\n", "\n", " def rank_dbpedia_results(self,results,term):\n", " \"\"\"\n", " logic:\n", " if edit distance 0, exact match\n", " if edit distance 1-4, good-partial match\n", " if edit distance > 4, partial match (results unsorted)\n", " \"\"\"\n", "\n", " matches = []\n", " for result in results:\n", " matches.append([metrics.edit_distance(self.normalize(result['term']), self.normalize(term)), result])\n", "\n", " matches.sort()\n", " #print matches\n", " if len(matches) == 0:\n", " return [ { 'match': 'none', 'term': term.encode('utf-8') } ] \n", " \n", " elif matches[0][0] == 0:\n", " new_results = [ matches[0][1] ]\n", " new_results[0]['match'] = 'exact'\n", " return new_results\n", "\n", " elif matches[0][0] <= 8:\n", " new_results = []\n", " for match in matches:\n", " if match[0] <= 3:\n", " result = match[1]\n", " result['match'] = 'good-partial'\n", " new_results.append(result)\n", "\n", " \"\"\" # needs refinement to provide good matches\n", " elif match[0] <= 5:\n", " words = self.normalize(result['term']).split(' ')\n", " if self.normalize(term) in words:\n", " result = match[1]\n", " result['match'] = 'good-partial'\n", " new_results.append(result)\n", " \"\"\"\n", " return new_results\n", "\n", " else:\n", " new_results = []\n", " for result in results[0:2]:\n", " result['match'] = 'partial'\n", " new_results.append(result)\n", " return new_results\n", " \n", " def wiki_url(self,url):\n", "\n", " term = url[url.rfind('/'):]\n", " entity_page = 'http://dbpedia.org/data/{}.json'.format(term)\n", " \n", " wiki_type = 'http://xmlns.com/foaf/0.1/primaryTopic'\n", " \n", " try:\n", " response = urlopen(entity_page)\n", " except:\n", " return\n", " \n", " data = json.loads(response.read())\n", " for key,value in data.items():\n", " 'http://xmlns.com/foaf/0.1/primaryTopic'\n", " if 'http://xmlns.com/foaf/0.1/primaryTopic' in value:\n", " #print key\n", " return key.encode('utf-8')\n", "\n", " def get_wiki_url(self, term):\n", " \n", " results = self.check_dbpedia(term)\n", "\n", " for result in results:\n", " if result['match'] != 'none' and result['match'] != 'partial':\n", " wiki = self.wiki_url(result['url'])\n", " result['wiki_url'] = wiki\n", "\n", " return results\n", "\n", "class WikiUrlFetchNonDBPedia():\n", "\n", " def __init__(self):\n", " self.wiki_api = 'http://en.wikipedia.org/w/api.php?action=query&list=search&format=json&srsearch='\n", " self.cleaned_term = None\n", "\n", " def fetch_wiki(self,term):\n", " self.cleaned_term = self.clean_term(term)\n", " self.results = self.get_wiki_url(self.cleaned_term)\n", " return self.results\n", "\n", " def check_wikipedia_api(self,term):\n", "\n", " url = self.wiki_api+re.sub(' ','_',term)\n", " request = urllib2.Request(url)\n", " request.add_header('User-Agent', 'Mozilla/5.0')\n", "\n", " try:\n", " response = urlopen(request)\n", " except urllib2.HTTPError, e:\n", " print e.code\n", " return\n", " except urllib2.URLError, e:\n", " print e.reason\n", " return\n", "\n", " results = []\n", " data = json.loads(response.read())\n", " for key,value in data.items():\n", " if 'search' in value:\n", " if type(value['search']) == list:\n", " results = self.parse_wikipedia_results(value['search'])\n", "\n", " if len(results) > 0:\n", " return self.rank_dbpedia_results(results,term)\n", " else:\n", " return [ { 'match': 'none', 'term': term.encode('utf-8') } ]\n", "\n", " def parse_wikipedia_results(self,results):\n", " base_wikipedia_url = 'http://en.wikipedia.org/wiki/'\n", " wiki_urls = []\n", " for result in results:\n", " if 'title' in result:\n", " wiki_urls.append( \\\n", " { 'url': (base_wikipedia_url + re.sub(' ','_',result['title'])).encode('UTF-8') , \\\n", " 'term': result['title'].encode('UTF-8') } )\n", "\n", " return wiki_urls\n", "\n", " def clean_term(self,term):\n", " return re.sub(r\"[^A-Za-z0-9 ]\",\"\",term.lower())\n", "\n", " def normalize(self,string):\n", " strings = string.lower().split(\" \")\n", " strings.sort()\n", " return \" \".join(strings)\n", "\n", " def rank_dbpedia_results(self,results,term):\n", " \"\"\"\n", " logic:\n", " if edit distance 0, exact match\n", " if edit distance 1-4, good-partial match\n", " if edit distance > 4, partial match (results unsorted)\n", " \"\"\"\n", "\n", " matches = []\n", " for result in results:\n", " matches.append([metrics.edit_distance(self.normalize(result['term']), self.normalize(term)), result])\n", "\n", " matches.sort()\n", " print term,matches\n", " print\n", " if len(matches) == 0:\n", " return [ { 'match': 'none', 'term': term.encode('utf-8') } ] \n", "\n", " elif matches[0][0] == 0:\n", " new_results = [ matches[0][1] ]\n", " new_results[0]['match'] = 'exact'\n", " return new_results\n", "\n", " elif matches[0][0] <= 5:\n", " new_results = []\n", " for match in matches:\n", " if match[0] <= 3:\n", " result = match[1]\n", " result['match'] = 'good-partial'\n", " new_results.append(result)\n", "\n", " # needs refinement to provide good matches\n", " elif match[0] <= 5:\n", " words = self.normalize(result['term']).split(' ')\n", " if self.normalize(term) in words:\n", " result = match[1]\n", " result['match'] = 'good-partial'\n", " new_results.append(result)\n", "\n", " return new_results\n", "\n", " else:\n", " new_results = []\n", " for result in results[0:2]:\n", " result['match'] = 'partial'\n", " new_results.append(result)\n", " return new_results\n", "\n", " def get_wiki_url(self, term):\n", "\n", " results = self.check_wikipedia_api(term)\n", "\n", " for result in results:\n", " if result['match'] != 'none' and result['match'] != 'partial':\n", " result['wiki_url'] = result['url']\n", " \n", "\n", " #print results\n", " return results" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Wiki2Plain Class" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# source: http://stackoverflow.com/questions/4460921/extract-the-first-paragraph-from-a-wikipedia-article-python/4461624#4461624\n", "#!/usr/bin/env python\n", "\n", "import re\n", "import yaml\n", "import urllib\n", "import urllib2\n", "\n", "class WikipediaError(Exception):\n", " pass\n", "\n", "class Wikipedia:\n", " url_article = 'http://%s.wikipedia.org/w/index.php?action=raw&title=%s'\n", " url_image = 'http://%s.wikipedia.org/w/index.php?title=Special:FilePath&file=%s'\n", " url_search = 'http://%s.wikipedia.org/w/api.php?action=query&list=search&srsearch=%s&sroffset=%d&srlimit=%d&format=yaml'\n", " \n", " def __init__(self, lang):\n", " self.lang = lang\n", " \n", " def __fetch(self, url):\n", " request = urllib2.Request(url)\n", " request.add_header('User-Agent', 'Mozilla/5.0')\n", " \n", " try:\n", " result = urllib2.urlopen(request)\n", " except urllib2.HTTPError, e:\n", " raise WikipediaError(e.code)\n", " except urllib2.URLError, e:\n", " raise WikipediaError(e.reason)\n", " \n", " return result\n", " \n", " def article(self, url):\n", " article = url[url.rfind('/')+1:]\n", " url = self.url_article % (self.lang, urllib.quote_plus(article))\n", " content = self.__fetch(url).read()\n", " \n", " if content.upper().startswith('#REDIRECT'):\n", " match = re.match('(?i)#REDIRECT \\[\\[([^\\[\\]]+)\\]\\]', content)\n", " \n", " if not match == None:\n", " return self.article(match.group(1))\n", " \n", " raise WikipediaError('Can\\'t found redirect article.')\n", " \n", " return content\n", " \n", " def image(self, image, thumb=None):\n", " url = self.url_image % (self.lang, image)\n", " result = self.__fetch(url)\n", " content = result.read()\n", " \n", " if thumb:\n", " url = result.geturl() + '/' + thumb + 'px-' + image\n", " url = url.replace('/commons/', '/commons/thumb/')\n", " url = url.replace('/' + self.lang + '/', '/' + self.lang + '/thumb/')\n", " \n", " return self.__fetch(url).read()\n", " \n", " return content\n", " \n", " def search(self, query, page=1, limit=10):\n", " offset = (page - 1) * limit\n", " url = self.url_search % (self.lang, urllib.quote_plus(query), offset, limit)\n", " content = self.__fetch(url).read()\n", " \n", " parsed = yaml.load(content)\n", " search = parsed['query']['search']\n", " \n", " results = []\n", " \n", " if search:\n", " for article in search:\n", " title = article['title'].strip()\n", " \n", " snippet = article['snippet']\n", " snippet = re.sub(r'(?m)<.*?>', '', snippet)\n", " snippet = re.sub(r'\\s+', ' ', snippet)\n", " snippet = snippet.replace(' . ', '. ')\n", " snippet = snippet.replace(' , ', ', ')\n", " snippet = snippet.strip()\n", " \n", " wordcount = article['wordcount']\n", " \n", " results.append({\n", " 'title' : title,\n", " 'snippet' : snippet,\n", " 'wordcount' : wordcount\n", " })\n", " \n", " # yaml.dump(results, default_style='', default_flow_style=False,\n", " # allow_unicode=True)\n", " return results\n", "\n", "class Wiki2Plain:\n", "# url_article = 'http://%s.wikipedia.org/w/index.php?action=raw&title=%s'\n", " url_image = 'http://%s.wikipedia.org/w/index.php?title=Special:FilePath&file=%s'\n", "# url_search = 'http://%s.wikipedia.org/w/api.php?action=query&list=search&srsearch=%s&sroffset=%d&srlimit=%d&format=yaml'\n", " \n", " def __init__(self, url):\n", " self.wiki = Wikipedia('en')\n", " self.wiki_article = self.wiki.article(url)\n", "\n", " self.text = self.wiki_article\n", " self.text = self.unhtml(self.text)\n", " self.text = self.unwiki(self.text)\n", " self.text = self.punctuate(self.text)\n", " self.text = self.get_summary(self.text)\n", " \n", " def __str__(self):\n", " return self.text\n", " \n", " def unwiki(self, wiki):\n", " \"\"\"\n", " Remove wiki markup from the text.\n", " \"\"\"\n", " wiki = re.sub(r'(?i)\\{\\{IPA(\\-[^\\|\\{\\}]+)*?\\|([^\\|\\{\\}]+)(\\|[^\\{\\}]+)*?\\}\\}', lambda m: m.group(2), wiki)\n", " wiki = re.sub(r'(?i)\\{\\{Lang(\\-[^\\|\\{\\}]+)*?\\|([^\\|\\{\\}]+)(\\|[^\\{\\}]+)*?\\}\\}', lambda m: m.group(2), wiki)\n", " wiki = re.sub(r'\\{\\{[^\\{\\}]+\\}\\}', '', wiki)\n", " wiki = re.sub(r'(?m)\\{\\{[^\\{\\}]+\\}\\}', '', wiki)\n", " wiki = re.sub(r'(?m)\\{\\|[^\\{\\}]*?\\|\\}', '', wiki)\n", " wiki = re.sub(r'(?i)\\[\\[Category:[^\\[\\]]*?\\]\\]', '', wiki)\n", " wiki = re.sub(r'(?i)\\[\\[Image:[^\\[\\]]*?\\]\\]', '', wiki)\n", " wiki = re.sub(r'(?i)\\[\\[File:[^\\[\\]]*?\\]\\]', '', wiki)\n", " wiki = re.sub(r'\\[\\[[^\\[\\]]*?\\|([^\\[\\]]*?)\\]\\]', lambda m: m.group(1), wiki)\n", " wiki = re.sub(r'\\[\\[([^\\[\\]]+?)\\]\\]', lambda m: m.group(1), wiki)\n", " wiki = re.sub(r'\\[\\[([^\\[\\]]+?)\\]\\]', '', wiki)\n", " wiki = re.sub(r'(?i)File:[^\\[\\]]*?', '', wiki)\n", " wiki = re.sub(r'\\[[^\\[\\]]*? ([^\\[\\]]*?)\\]', lambda m: m.group(1), wiki)\n", " wiki = re.sub(r\"''+\", '', wiki)\n", " wiki = re.sub(r'(?m)^\\*$', '', wiki)\n", " \n", " return wiki\n", " \n", " def unhtml(self, html):\n", " \"\"\"\n", " Remove HTML from the text.\n", " \"\"\"\n", " html = re.sub(r'(?i) ', ' ', html)\n", " html = re.sub(r'(?i)', '\\n', html)\n", " html = re.sub(r'(?m))?\\s*([^\\\\/:*?<>\"|%]+\\.[^\\\\/:*?<>\"|%]{3,4})', self.wiki)\n", " match = re.search(r'= (\\b[\\w ]+\\b)+.(gif|jpg|jpeg|png|bmp)', self.wiki_article)\n", " if match:\n", " image_url = url_image + '%s.%s' % match.groups()\n", " image_url = re.sub(' ', '_', image_url)\n", " return image_url\n", " \n", " return None" ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }