T. Flati, D. Vannella, T. Pasini, R. Navigli 2 Is Bigger (and Better) Than 1: the Wikipedia...

T. Flati, D. Vannella, T. Pasini, R. Navigli

2 Is Bigger (and Better) Than 1: the Wikipedia Bitaxonomy Project

ERC Starting GrantMultiJEDI No. 259234

The Wikipedia structure

Article pages~4M

Category pages~ 700K

Two noisy graphs with no explicit hypernym relation.

The Wikipedia structure: an examplePages Categories

Mickey Mouse

Funny AnimalSuperman

Cartoon

Donald Duck

Disney comics characters

Disney comicsDisney character

Fictional characters by

medium

Comics by genre

Fictional characters

The Walt Disney Company

Our goal

To automatically create a Wikipedia Bitaxonomy for Wikipedia pages and categories in a

simultaneous fashion.

pages categories

Our goal

To automatically create a Wikipedia Bitaxonomy for Wikipedia pages and categories in a

simultaneous fashion.

The page and category level are mutually beneficial for inducing a wide-coverage and fine-grained integrated taxonomy

KEY IDEA

Key idea Pages Categories


Disney comicsDisney character

The Walt Disney Company

Fictional characters by

medium

Comics by genre

Fictional characters

Mickey Mouse

Funny AnimalSuperman

Cartoon

Donald Duckis a

is a

is a

is a

is a

is a

is ais a is a

A 3-phase method

pages categories

Starting from two noisy graphs

A 3-phase method1. Build the page taxonomy

pages

A 3-phase method1. Build the page taxonomy2. Bitaxonomy Algorithm

pages categories

A 3-phase method

pages categories

1. Build the page taxonomy2. Bitaxonomy Algorithm

pages

1. Build the page taxonomy

A 3-phase method

+50%categories

categories

3. Refine the category taxonomy2. Bitaxonomy Algorithm

Contributions

1. Self-contained approach

2. Page taxonomy and category taxonomy built simultaneously

3. State-of-the-art results when compared to all other available taxonomies

The WiBi Page taxonomy1

Assumptions

• The first sentence of a page is a good definition (also called

gloss)

The WiBi Page taxonomy

1. [Syntactic step]Extract the hypernym lemma from a page definition using a syntactic parser;

2. [Semantic step]Apply a set of linking heuristics to disambiguate the extracted lemma.

Scrooge McDuck is a character […]

Syntactic step

Hypernym lemma: character

A

Semantic step

Scrooge McDuck is a character[…]nn nsubj

cop

The semantic step

5 cascadinglinking heuristics

Ambiguoushypernym(‘player’)

Linking heuristic

Target page(Cristiano Ronaldo)

Disambiguatedhypernym

(Football player)

1. Crowdsourced2. Category3. Multiword4. Monosemous5. Distributional

1. Crowdsourced heuristic

Mickey Mouse is a funny animal cartoon character and the official mascot ofThe Walt Disney Company.

Use the links from the crowd!

Given a page and its ambiguous hypernym, exploit its categories to build a distribution of the hypernym’s senses.

Characters in Disney package films

Disney comics charactersAmbiguous

hypernym: Character

Donald Duck Pluto

Hook

Mickey Mouse

José Carioca

2. Category heuristic

Goofy

2. Category heuristicGiven a page and its ambiguous hypernym, exploit its categories to build a distribution of the hypernym’s senses.

Donald Duck Pluto

Hook

Mickey Mouse

José Carioca

Goofy

Goofy is a funny animal cartoon character […]

José Carioca is a Disney cartoon character […]

Captain James Hook is a fictional character […]

Mickey Mouse is a funny animal cartoon character […]

Pluto, also called Pluto the Pup, is a cartoon character […]



Disney comics charactersAmbiguous

hypernym: Character


Donald Duck

Goofy is a funny animal cartoon character […]

José Carioca is a Disney cartoon character […]

Captain James Hook is a fictional character […]


Pluto, also called Pluto the Pup, is a cartoon character […]


Character (arts) 5, Funny animal 1

Character (arts) 3, Funny animal 1, Cartoon 1

Character(arts) 8, Funny animal 2, Cartoon 1Ambiguous hypernym: Character



Character(arts) 8, Funny animal 2, Cartoon 1


Donald Duck

Character(arts)Ambiguous hypernym: Character

Page taxonomy linking heuristics

Category(1.603M)

Multiword(65K) Monosemous

(161K)

Distributional(561K)

Crowdsourced(1.338M)

1

2

34

5

Page taxonomy evaluation

The story so far

1

Noisy page graph Page taxonomy

2The Bitaxonomyalgorithm

The Bitaxonomy algorithm

The information available in the two taxonomies is mutually beneficial;● At each step exploit one taxonomy to update

the other and vice versa;● Repeat until convergence.

pages categories

Real MadridF.C.

Football team Football teams

Football clubsin Madrid

is a

Atlético Madrid


Football clubs

Starting from the page taxonomy

Real MadridF.C.



is a

is a


Football clubs

Exploit the cross links to infer hypernym relations in the category taxonomy

Atlético Madrid

pages categories

Real MadridF.C.



is a

is a

is a


Football clubs

Take advantage of cross links to infer back is-a relations in the page taxonomy

Atlético Madrid

pages categories

Real MadridF.C.



is a

is a

is a


Football clubs

is a

Use the relations found in previous step to infer new hypernym edges

Atlético Madrid

pages categories

Atlético MadridReal Madrid

F.C.



is a

is a

is a


Football clubs

is a

Mutual enrichment of both taxonomies until convergence

pages categories

Page taxonomy evaluation (cont’d)Sensible 3% increment in terms of recall and coverage,with unvaried precision

Category taxonomy evaluation

The story so far

2

3The WiBi category taxonomy refinement

Comics characters by protagonist

Comics characters

Garfield characters

Category taxonomy refinement

Some categories are affected by some structural problems.

pages categories

No pagesassociated!

Category taxonomy refinement● 3 refinement procedures to obtain broader

coverage for categorieso Single super categoryo Sub-categorieso Super-categories

Single super category

This category has only 1 outgoing edge

Comics characters by protagonist

Comics characters

Garfield characters

Animated television characters by series

Animated characters

Fictional characters by medium

Animation

So we promote its only super category to hypernym

Sub-categories

Comics characters by company

Disney comics

Comics by companyComics characters

DC Comicscharacters

Marvel Comicscharacters

Comics titlesby company

Focus on subcategories which have already been covered!

Sub-categories

Comics characters by company

Disney comics

Comics by companyComics characters

DC Comicscharacters

Comics titlesby company

Marvel Comicscharacters

Focus on subcategories which have already been covered!

Only 1 path ending in u

2 pathsending in v

Category taxonomy evaluation: coverage

+50%categoriescovered!

1SUP SUB SUPER

Category taxonomy evaluation: P & R

Iterations1SUP SUB SUPER

+35%recall

86%

Experimental setup

● We created 2 datasets:o 1000 randomly sampled pages;o 1000 randomly sampled categories.

● Each item was annotated with the most suitable generalization (lemma+page or category).

Competitors

WikiNet

MENTA

WikiTaxonomy

pages categories

Measures

● We calculated typical measures to assess the quality of all the possible taxonomies;o Precisiono Recallo Coverageo Specificityo Granularity

Page taxonomy comparison

Category taxonomy comparison

Category taxonomy comparison

Specificitymeasure

Measuring specificityA system is more specific than another when the hypernym(s) provided by the former are more specific/informative than the latter.

System 1

“Singer”System 2

“Swing singer”

“Frank Sinatra is a”

<less specific than

Page taxonomy specificityRatio of the times in which WiBi provided

a more specificanswer than the other system

Page taxonomy specificityRatio of the times in which WiBi

provided a less specific answer than the other system

Category taxonomy specificity

Measuring granularity

pages categories

Conclusions● Unified, 3-phase approach

to the construction of a bitaxonomyfor the English Wikipedia;

● Self-contained, no additionalresources or supervision required;

● Nearly full coverage of Wikipedia pages and categories;● State-of-the-art performance both on pages and categories.

wibitaxonomy.org

Tiziano Flati, Daniele Vannella, Tommaso Pasini, Roberto Navigli

Linguistic Computing Laboratorylcl.uniroma1.it

T. Flati, D. Vannella, T. Pasini, R. Navigli 2 Is Bigger (and Better) Than 1: the Wikipedia...

Documents

Transcript of T. Flati, D. Vannella, T. Pasini, R. Navigli 2 Is Bigger (and Better) Than 1: the Wikipedia...