Machine Learning for Knowledge Extraction from Wikipedia & Other Semantically Weak Sources - OSCON 2008

We all love Wikipedia!

Wikipedia has lots of data...

Lots of semi-structured data!

At Freebase, we use Wikipedia as a source for extracting facts and relationships

Some Interesting Data in Wikipedia Infoboxes Categories Text

Problems With Wikipedia Data •The data is dirty •Wiki markup is hard to parse.. •.. and often dirty •.. and not well deﬁned •Properties and relations are in Wiki markup •Page redirects have to be resolved

{{Infobox Company | company_name = Sony Corporation | company_logo = [[Image:Sony logo.svg|220px|]] | slogan = like.no.other | company_type = [[Public company|Public]] ({{Tyo|6758}}) ({{nyse|SNE}}) | foundation = [[May 7]] [[1946]] (adopted current name in 1958)<ref name=sonycorpinfo>{{citeweb|url=http://www.sony.net/SonyInfo/ CorporateInfo/|title=Sony Global - Corporate Information| accessdate=2007-07-24}}</ref> | founder = [[Masaru Ibuka]] [[Akio Morita]] | location = {{flagicon|Japan}} [[Minato, Tokyo]], [[Japan]]<ref name="sonycorpinfo"/> | area_served = [[Worldwide]] | key_people = [[Sir Howard Stringer]] ([[Chairman]]) & ([[CEO]])<ref name="sonycorpinfo"/> [[Ryoji Chubachi]] ([[President]]) & ([[CEO|Electronics CEO]])<ref name="sonycorpinfo"/> | industry = [[Consumer electronics]] [[Entertainment]] | products = [[Audio]] [[Video]] [[Televisions]] [[Information Technology| Communications and Information Technology]] [[Semiconductors]] [[Electronic components]] [[Motion Picture]] [[Music]] [[Online|Online Business]] [[Sony Playstation]] | services = [[Financial services]] | market cap = [[United States Dollar|US$]] 40.56 Billion (''2008'') | revenue = {{profit}} [[United States Dollar|US$]] 88.714 Billion (''2008'')<ref name="2007 Q4">{{citeweb|url=http://www.sony.net/SonyInfo/ IR/financial/fr/07q4_sony.pdf|title=Sony Corporation Earnings release for the fiscal year ended March 31, 2008|format=PDF}}</ref> | operating_income = {{profit}} [[United States Dollar|US$]] 3.745 Billion (''2008'') | net_income = {{profit}} [[United States Dollar|US$]] 3.694 Billion (''2008'') | assets = {{increase}} [[United States Dollar|US$]] 117.603 Billion (''2008'') | equity = {{increase}} [[United States Dollar|US$]] 32.465 Billion (''2008'') | num_employees = 180,500 (as of [[March 31]] [[2008]]) <ref name="sonycorpinfo"/> | parent = | subsid = [[Sony Corporation shareholders and subsidiaries|List of the subsidiaries]]

Problems With Wikipedia Data •Wikipedia is huge! •2,150,00 articles •7,100,000 category references •found in 280,000 categories •54,029 non-trivial templates (>= 5 uses) •50,671,533 Template name-value properties •Wikipedia is growing! •Grows 2% a week •25,170 new articles •39,571 new redirects •8,000 deletes •5,000 name changes •1,000 article splits •1,000 id changes •Before this talk is over, there will be 150 NEW articles!

The Freebase Wikipedia Extraction (WEX) Current Current Current Current Wikipedia Current Wikipedia Wikipedia Wiki2XML Parser Wikipedia Dump Markup Wiki Wikipedia Dump Markup XML Dump Dump Articles Dump Articles Magnus Manske Big Sections Templates Text Postgres Articles Database! Freebase Redirects Categories Mappings

WEX Article XML <template name="Infobox_President"> <param name="name">Abraham Lincoln</param> <param name="nationality">American</param> <param name="image">Abraham Lincoln head on shoulders photo portrait.jpg</param> <param name="order">16th<space/><link> <target>President of the United States</target></link></param> <param name="term_start"><link><target>March 4</target></link>, <space/><link><target>1861</target></link></param> <param name="term_end"><link><target>April 15</target></link>, <space/><link><target>1865</target></link></param> <param name="predecessor"><link><target>James Buchanan</target></link></param> <param name="successor"><link><target>Andrew Johnson</target></link></param> .... </template>

WEX Schema category_ members articles sections template_ redirects calls template_ freebase_ values wpid freebase_ freebase_ types names

SELECT xpath('/param/text()', template_values.xml) FROM template_values INNER JOIN template_calls ON call_id = template_calls.id INNER JOIN articles ON articles.wpid = article_wpid WHERE template_article_name = 'Template:Infobox Bridge' AND template_values.name = 'mainspan' AND articles.name = 'Fremont Bridge (Portland)' Result: "{"1,255 ft (382.5 m)","longest in Oregon"}"

words become “features”

category_list=['Category:Ninjas', 'Category:Pirates', 'Category:Assassins', 'Category:Apple Inc. employees', 'Category:Microsoft employees', 'Category:Google employees', 'Category:Free software programmers', 'Category:Computer programmers'] print "Training classifiers..." # Get the members of every category name_classes={} for category in category_list: members = get_category_members(cur, category) print str(len(members)) + " examples for " + category for name in members: name_classes.setdefault(name,set()).add(category)

def get_category_members(cur, category): records = [] queue = [category] recordsSeen = set() while len(queue) > 0 and len(records) < 500: currentCategory = queue.pop(0) cur.execute("select articles.wpid, articles.name " + "from wikipedia.category_members, wikipedia.articles " + "where category_members.category_name like %s and " + "articles.wpid=category_members.article_wpid", (currentCategory,)) result = cur.fetchall() for wpid, name in result: if wpid not in recordsSeen: recordsSeen.add(wpid) if name.startswith("Category:"): queue.append(name) else: records.append(name) return records

for name in name_classes: name, text = get_article_text(cur, name) words=getwords(text[0:1024]) for cat,cl in classifiers: if cat in name_classes[name]: cl.train(words,1) else: cl.train(words,0)

def get_article_text(cur, name): cur.execute("select name, text from " + "wikipedia.articles where name=%s", (name,)) return cur.fetchone()

# Test set: test_set=["Henri Caesar", "Long John Silver", "Jack Sparrow", "Storm Shadow (G.I. Joe)", "Leonardo (TMNT)", "Bill Gates", "Steve Jobs", "Richard Stallman", "Larry Page", "Guido van Rossum", "Larry Wall", "Jerry Yang"] # Run tests: for testName in test_set: name, text = get_article_text(cur, testName) print name words=getwords(text[0:1024]) for cat,cl in classifiers: py,pn=cl.prob(words,1),cl.prob(words,0) print '%st%st%f' % (cat,cl.classify(words),py/pn if pn>0 else 100)

Category:Ninjas 0 0.000000 Category:Pirates 1 155082805066493952.000000 Category:Assassins 0 0.000001 Category:Apple Inc. employees 0 0.000000 Category:Microsoft employees 0 0.000000 Category:Google employees 0 0.000000 Category:Free software programmers 0 0.000000 Category:Computer programmers 0 0.000000

Category:Ninjas 1 166627867883323968.000000 Category:Pirates 1 19.751727 Category:Assassins 1 413388475722811.625000 Category:Apple Inc. employees 0 0.000000 Category:Microsoft employees 0 0.000000 Category:Google employees 0 0.000000 Category:Free software programmers 0 0.000000 Category:Computer programmers 0 0.000000

ninja japan characters period sengoku service they use means term based different japanese era heroes appearance kanji

pirate coast ship pirates crew off sea captured island century ships piracy north merchant captain according

can we construct a sentence that ﬁts into both categories? (without using the word “pirate” or “ninja”)

“Toby Segaran lived during the sengoku period in Japan. He spent many years at sea capturing Japanese ships.”

http://code.google.com/p/wexbayes/

Category:Ninjas 0.008158 Category:Pirates 21125425312885.750000 Category:Assassins 237.408562 Category:Apple Inc. employees 0.000000 Category:Microsoft employees 0.000000 Category:Google employees 0.000000 Category:Free software programmers 0.000000 Category:Computer programmers 0.000000

Category:Ninjas 2924533519139.380859 Category:Pirates 0.003120 Category:Assassins 67800277337186.242188

Category:Ninjas 300.861827 Category:Pirates 8392781192.375138 Category:Assassins 3817.111709

Category:Ninjas 0.000000 Category:Pirates 0.000000 Category:Assassins 0.000000 Category:Apple Inc. employees 63.870863 Category:Microsoft employees 186751.882154 Category:Google employees 0.012458 Category:Free software programmers 0.000197 Category:Computer programmers 1222.542202

Category:Apple Inc. employees 66414979293154.234375 Category:Microsoft employees 694373.180082 Category:Google employees 2381.361809 Category:Free software programmers 0.014712 Category:Computer programmers 871493.654163

Machine Learning for Knowledge Extraction from Wikipedia & Other Semantically Weak Sources - OSCON 2008

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