Inducing Ontologies from Folksonomies using Natural Language Understanding

Marta Tatu, Dan MoldovanLymba Corporation

Presenter: Chris Irwin Davis

Overview

LREC 2010 May 19th, 2010

NLP

Folksonomy

• typographical errors, spelling variations• singular/plural forms, lower case• space/punctuation used as delimiters• same tag in different contexts• tag synonymy

Ontology

• lexical normalization of tags• semantic consistency• tag-tag relations

social annotations (author vs. user) browse/search bookmarks resource discovery (recommendations) collaborative tagging (across folksonomies)

folksonomy-based applications reasoning applications

Semantic Approach

1. Folksonomy semantic representation

2. Tag understandingo Lexical: language identification, tokenization and spelling corrections, capitalization

restoration

o Syntactic: part-of-speech tagging, syntactic parsing

o Semantic: acronym understanding, word sense disambiguation, named entity recognition, semantic parsing

3. Deriving the ontological structureo Semantic relations between tags

• Sources of informationo Tag text semantics

o Social bookmarking annotations

o Machine understanding of bookmark content

LREC 2010 May 19th, 2010

Representing Folksonomies

• knowledge

• advertisign

• americanhistory

• read-now

LREC 2010 May 19th, 2010

American[JJ]1 history[NN]2TOPIC

now[RB]3 read[VB]1TEMPORAL

advertising[NN]1

knowledge[NN]1

Representing Folksonomies

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SYNONYMY cluster

knowledge

(axlape,www.wolframalpha.com/)(nicksoni,www.curatingthecity.org/map.jsp)(pilx,www.wolframalpha.com/)...

knowledge|NN|1

knowledge,cognition

(bernsnarok,www.wolframalpha.com/)(_tarea_,academicearth.org/)(_tarea_,www.howstuffworks.com/)...

(omnamoprabhu,www.goertzel.org/dynapsyc/dynacon.html)(MikeMolto,cvcl.mit.edu/)(latrippi,nymag.com/news/features/56793/)...

cognition|NN|1

folksonomic tags

associated (user,document) pairs

WN synsetId = 20729

Associated (user, document) pairs

Representing Folksonomies

cognition|NN|1; knowledge|NN|1

module|NN|1; faculty|NN|1 organization|NN|1; organisation|NN|2 pattern|NN|1; form|NN|3

ISA ISA ISA

cognitive|JJ|1 PERTAIN perception|NN|1

PW

design|NN|2

ISA

calendar|NN|1

ISA

ISAISA

PDA|NN|1 – Personal Digital Assistant

organization|NN|1; governance|NN|1

SIM

SIM

SIM

adaptive|JJ|1 design|NN|2PAH

instructional|JJ|1 design|NN|2AGT

LREC 2010 May 19th, 2010

System Architecture

NLP processing

Document Cache

Document NLP

Repository

Social Tag-Tag & Tag-Doc Associations

Lexical Processing

of Tags

Syntactic Processing

of Tags

Semantic Processing

of Tags

Social Annotations

user

document

tag

Doc-2-TextLanguage IdentificationNLP of EN documents: Tokenization Part-of-speech tagging Sentence boundary detection Named entity recognition Syntactic parsing Word sense disambiguation Semantic parsing

Semantic Representation

of Tags

Ontology generation(Tag-Tag relations)

Applications: Search, browse, visualize Recommendations Collaborative tagging

Tag Classification Rules

Induced Ontology

LREC 2010 May 19th, 2010

Tag Understanding

Sources used to understand tags

Tag text Social bookmarking data Document content

Lexical

Language identification X X X

Tokenization and Spell checking X X X

Capitalization restoration X X

SyntacticPart-of-speech tagging X X

Syntactic parsing X

Semantic

Abbreviation and acronym expansion X X X

Word sense disambiguation (+ ner) X X X

Semantic parsing X

LREC 2010 May 19th, 2010

Acronym/Abbreviation Understanding• Abbreviation dictionary: (abbreviation - expansion - domain of usage)

o 118,055 distinct abbreviations

o 137 domains: Law, Music, TV/Radio Stations, Countries, Airport, Domain Names, Chat, Emoticons, etc.

o 25% of the abbreviations have more than one definition

• (unambiguous) Zip codes – (76012 : Arlington, TX)

• (ambiguous) SS : 192 definitions in 66 domains

o Social Security – Business and US Government, Screen Saver – File Extensions, Stainless Steel – Housing and Products, Subtropical Storm – Meteorology, Style Sheet – Software

• Check tag if part of abbreviation dictionary

• Use lexical chains to link document content to abbreviation domain

• Use co-occurring tags to identify correct expansion

• Use text alignment to find new abbreviation definitions within document content

LREC 2010 May 19th, 2010

Acronym/Abbreviation Understanding• “PR” ~ 1409 documents

• 87 definitions for PR

o Press Release, Public Relations, Puerto Rico, Page Rank, Public Radio, Permanent Resident/Residency, etc.

• http://prsarahevans.com/2009/06/do-you-have-a-strategy-for-online-comments

o “PR” = “public relations” (6 times in document content)

o Other tags of the bookmark: “public”, “relations”, “media”, “strategy”

• http://www.bbc.co.uk/pressoffice/pressreleases/category/new_media_index.shtml

o “PR” = “press releases” (in document content)

• http://escape.topuertorico.com

o “PR” = “Puerto Rico” (in document content)

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Evaluation

• Experimental datao ~ 150,000 (user,document,tag) from del.icio.us

• 8,460 tags; 83,827 documents; 58,198 users

• Main error source: tag cannot be identified within documento Lack of document content (images, non-EN content, etc.)

• Errors propagate from initial processing steps to later oneso Bad capitalization leads to bad named entity recognition

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Ontological Tag-Tag Relations• EQUALITY relations

o same lemma, part-of-speech, and sense number

o EQ(activity, activities), EQ(after-effects, AfterEffects), EQ(opinion, Opnion), etc.

• SYNONYMY clusters

o Same synset id

o SYN(OS, operating.system), SYN(LA, losangeles), SYN (nyt, nytimes)

• ISA relations between named entities and type tags

o ISA(OracleCorporation, organization), ISA(davidfosterwallace, person)

• WordNet relations between tags

o ISA(vegan, vegetarian), ANTONYMY(peace, war), PART_WHOLE(Businesses, markets), ENTAIL(proofreading, +read), SIMILARITY(important, general), DOMAIN(light, physics)

LREC 2010 May 19th, 2010

Ontological Tag-Tag Relations• Lexical chains of size 2 and Semantic calculus

– tag1 rel1 synset rel2 tag2

• rel1 & rel2 rel3

• rel3(tag1, tag2) is added to the ontology

– ISA(integration, events,) ISA(integration, group_action/NN/1) and ISA(group_action/NN/1, events,)

– PART_WHOLE(lobby, hotels) PART_WHOLE(lobby, building/NN/1) and ISA(building/NN/1, hotels)

• ISA relations between “modifier head” and “head” tags

– ISA(book-cover, covers)

– ISA(theoryofmind, theory)

– ISA(photoshoptutorials, tutorials,)

LREC 2010 May 19th, 2010

Ontological Tag-Tag Relations

• Relations between “modifieri headi” tags (i=1,2)

– ISA(build-solar-panel, create-solar-panel)

– SIMILARITY(socialnetworks, socialweb)

LREC 2010 May 19th, 2010

modifier2

modifier1

ISA

head2

head1

ISA

modifier2

modifier1

ISA

head2

head1

SYN

modifier2

modifier1

SYN

head2

head1

ISA& & &OR OR

head2

modifier2

REL

head2

modifier2

REL

ISA⇒

Evaluation

• 9,820 EQ clusters for the 8,460 unique tagso Same abbreviation expanded to different definitions

o EQ: tutorial, tutorials, tutorials,

• 8,801 SYN clusterso Largest cluster (133 bookmarks): car, automobiles, auto, autos, cars,

automobile

• 17% of tags placed into incorrect SYN clustero Errors caused by imperfect word sense disambiguation

• 5,439 ontological tag-tag relationso 3,869 ISA, 601 SIMILARITY, 429 PART_WHOLE, etc.

o 1,778 relations derived using WordNet’s lexical chains and Lymba’s semantic calculus rules

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Folksonomic Ontology

LREC 2010 May 19th, 2010

• Portion of ontology generated from experimental folksonomy

Folksonomic Ontology

LREC 2010 May 19th, 2010

• Portion of ontology generated from experimental folksonomy

Thank you!

For questions: email marta@lymba.com