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Modelling Users’ Profiles and Interests based on Cross-Folksonomy Analysis. Martin Szomszor University of Southampton. Outline. Introduction and Motivation Why is your folksonomy interaction useful? How could it be exploited? Making Sense of Folksonomies Distributed Contact Networks

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Modelling users profiles and interests based on cross folksonomy analysis

Modelling Users’ Profiles and

Interests based on

Cross-Folksonomy Analysis

Martin Szomszor

University of Southampton

TAGora: Semiotic Dynamics of Online Social Communities EU-IST-2006-034721


Outline

Outline

  • Introduction and Motivation

    • Why is your folksonomy interaction useful?

    • How could it be exploited?

  • Making Sense of Folksonomies

    • Distributed Contact Networks

    • Tag Filtering / Tag Senses

  • Profiles of Interests

  • Future Work

    • Disambiguation

    • Building Better Profiles of Interests


Introduction

Introduction

http://news.bbc.co.uk/

http://slashdot.org/

Dream Theater

Metallica

Rush

delicious.com


Increasing number of online identities

Increasing number ofonline identities

  • Recent Ofcom study found that UK adults have on average 1.6 profiles. 39% of those that have one profile have at least 2

    • [Ofcom 2008] Social Networking: A quantative and qualitative research report into attitudes, behaviours, and use.

  • In the future, people will maintain an increasing number of online identities to meet different information sharing tasks and to connect with different communities


Tag clouds

Tag Clouds

delicious.com


The big picture

The Big Picture

Profile of Interests

delicious.com


Personalisation

Personalisation

Profiles could be exported to other sites to improve recommendation quality

Profile of Interests

Better user experience

Profiles could be used to support personalised searching

delicious.com


Consolidation and integration

Consolidation and Integration

cuba

cuba

hotels

holiday

travel

2008

currency

http://dbpedia.org/resource/Cuba

http://dbpedia.org/resource/Travel

http://dbpedia.org/resource/Holiday

http://dbpedia.org/resource/Category:Tourism


Tagging variation

Tagging Variation

Filtered Tags

Raw Tags

[1] Szomszor, M., Cantador, I. and Alani, H. (2008). Correlating User Profiles from Multiple Folksonomies. In: ACM Conference on Hypertext and Hypermedia, 2008 , Pittsburgh, Pennsylvania.


Disconnected identities

Disconnected Identities

fan of

friend

#me

contact

friend


Making sense of folksonomies

Making Sense of Folksonomies

Tagging Semantics

FOAF

DBpedia + Wordnet

Identity Integration

Tag Integration

Delicious

Last.fm

Flickr

Facebook


1 contact integration

1. Contact Integration

Tagging Semantics

FOAF

DBpedia + Wordnet

Identity Integration

Tag Integration

Delicious

Last.fm

Flickr

Facebook


Modelling users profiles and interests based on cross folksonomy analysis

SNS Contact Integration


Modelling users profiles and interests based on cross folksonomy analysis

Consolidated Contact View

  • Recommend new connections

#me


Modelling users profiles and interests based on cross folksonomy analysis

FOAF Representation of SNS Accounts

http://tagora.ecs.soton.ac.uk/LiveSocialSemantics/ht2009/foaf/4

http://tagora.ecs.soton.ac.uk/facebook/613077109

<http://tagora.ecs.soton.ac.uk/facebook/613077109>

<http://tagora.ecs.soton.ac.uk/schemas/facebook#hasFriend>

<http://tagora.ecs.soton.ac.uk/facebook/1006466985>,

<http://tagora.ecs.soton.ac.uk/facebook/684541156>,

<http://tagora.ecs.soton.ac.uk/facebook/1043367866>;

<owl#sameAs>

http://tagora.ecs.soton.ac.uk/delicious/martinszomszor

http:[email protected]

http://tagora.ecs.soton.ac.uk/lastfm/mszomszor


2 tag integration

2. Tag Integration

Tagging Semantics

FOAF

DBpedia + Wordnet

Identity Integration

Tag Integration

Delicious

Last.fm

Flickr

Facebook


Folksonomy integration tag heterogeneity

Folksonomy IntegrationTag Heterogeneity

Web2.0

Web_2.0

!=


Folksonomy integration tag heterogeneity1

Folksonomy Integration:Tag Heterogeneity

isFilteredTo

Web2.0

Web_2.0


Tag filtering

Tag Filtering

  • Find canonical form for each tag:

    • Use Dbpedia entry labels as reference

      • compound terms separated by _

        • second-life, second+life, second.life -> second_life

      • concatenated / camel case terms are expanded

        • secondlife, SecondLife -> second_life

      • International Characters Normalised:

        • Caf%C3%A9 -> Cafe

  • Recommend Spelling Corrections

    • resaerch -> didYouMean research

  • Follow unambiguous redirections:

    • Humor, Funny -> Humour


Modelling users profiles and interests based on cross folksonomy analysis

cooccurringTag

isFilteredTo

Tag

xsd:string

CooccurrencInfo

hasCooccurrenceInfo

rdfs:label

xsd:integer

UserTag

xsd:integer

hasCooccurrenceFrequency

hasUserFrequency

hasDomainTag

tagUsed (f)

DomainTag

xsd:integer

hasNextSegment (f)

hasDomainFrequency

hasGlobalTag

TagSegment

GlobalTag

xsd:integer

hasGlobalFrequency

FinalTagSegment

hasTagSequence (f)

usesTag

Resource

hasPost

Post

taggedResource

Tagger

http://tagora.ecs.soton.ac.uk/schemas/tagging#

http://www.w3.org/2001/XMLSchema#

taggedOn

property

subclass

xsd:datetime

(f) = functional property


Linked data view

Linked Data View


Linked data view1

Linked Data View


Linked data view2

Linked Data View


Linked data view3

Linked Data View


Finding syntactic variations

sparql$ select ?x where {

?x

<http://tagora.ecs.soton.ac.uk/schemas/tagging#isFilteredTo>

<http://tagora.ecs.soton.ac.uk/tag/web_2.0>}

┌─────────────────────────────────────────────┐

│ ?x │

├─────────────────────────────────────────────┤

│ <http://tagora.ecs.soton.ac.uk/tag/web2.0> │

│ <http://tagora.ecs.soton.ac.uk/tag/web2> │

│ <http://tagora.ecs.soton.ac.uk/tag/web_2.0> │

│ <http://tagora.ecs.soton.ac.uk/tag/web_20> │

│ <http://tagora.ecs.soton.ac.uk/tag/web20> │

└─────────────────────────────────────────────┘

sparql$ select * where {

?x

<http://tagora.ecs.soton.ac.uk/schemas/tagging#isFilteredTo>

<http://tagora.ecs.soton.ac.uk/tag/second_life>}

┌───────────────────────────────────────────────────┐

│ ?x │

├───────────────────────────────────────────────────┤

│ <http://tagora.ecs.soton.ac.uk/tag/second_Life> │

│ <http://tagora.ecs.soton.ac.uk/tag/second.life> │

│ <http://tagora.ecs.soton.ac.uk/tag/SecondLife> │

│ <http://tagora.ecs.soton.ac.uk/tag/Second_Life> │

│ <http://tagora.ecs.soton.ac.uk/tag/second%20life> │

│ <http://tagora.ecs.soton.ac.uk/tag/SECOND_LIFE> │

│ <http://tagora.ecs.soton.ac.uk/tag/second_life> │

│ <http://tagora.ecs.soton.ac.uk/tag/secondlife> │

└───────────────────────────────────────────────────┘

Finding Syntactic Variations


Tag senses

Tag Senses

  • What are the possible meanings for a tag?

  • We use two reference sets:

    • DBPedia

      • Concepts

    • Wordnet

      • Synsets


Disambiguation ontology

Disambiguation Ontology

didYouMean

hasWordnetSense

Tag

WordSense

DbpediaSenseInfo

hasDbpediaSenseInfo

http://www.w3.org/2006/03/wn/wn20/schema/

senseWeight

http://tagora.ecs.soton.ac.uk/schemas/disambiguation#

http://tagora.ecs.soton.ac.uk/schemas/dbpedia#

dbpediaSense

http://tagora.ecs.soton.ac.uk/schemas/tagging#

http://www.w3.org/2001/XMLSchema#

property

subclass

xsd:float

(f) = functional property

Resource


Dbpedia extraction

DBpedia Extraction

  • Extract triples from XML dump

    • Calculate normalised title string

      • Caf%C3%A9 -> cafe

    • Calculate concatenated title string

      • Second_life -> secondlife

    • Extract disambiguation term from title

      • Orange_(fruit)

    • Identify compound labels

      • Second_Life -> Second, Life


Dbpedia extraction1

DBpedia Extraction

  • Number of incoming links

  • Extract page redirects

  • Extract Disambiguation Links

    • Find Primary disambiguation (e.g. Apple)


Dbpedia extraction2

DBpedia Extraction

  • Parse wiki text and extract terms:

    • Terms filtered using stop words (with some wiki specific additions)

    • Store term frequencies

    • Store number of distinct terms in page

    • Store total term frequency

  • Can associate a vector of terms and weights to each possible sense


Modelling users profiles and interests based on cross folksonomy analysis

hasNextLabelSequence (f)

hasCompoundLabelSequence (f)

CompoundLabelSequence

hasPrimaryDisambiguation

xsd:string

isa

hasDisambiguation

hasCompoundLabel (f)

FinalCompoundLabelSequence

Resource

hasLabel

xsd:string

xsd:string

hasNormalisedLabel

hasTermFrequencyPair

hasConcatenatedLabel

xsd:string

xsd:string

xsd:integer

xsd:integer

hasDisambiguationTerm

TermFrequencyPair

hasTotalTerms

hasTotalTermFrequency

hasTerm

xsd:string

hasTermFrequency

xsd:integer


Profiles of interests

[2] Szomszor, M., Alani, H., Cantador, I., O'Hara, K. and Shadbolt, N. (2008) Semantic Modelling of User Interests based on Cross-Folksonomy Analysis. In: 7th International Semantic Web Conference (ISWC), October 26th - 30th, Karlsruhe, Germany.

Profiles of Interests


Global category view

Global Category View

  • What are the differences in the interests that are learnt from each domain?


Future work

Future Work

  • Given a set of possible senses, how can we choose the best match?

  • Folksonomy data can provide contextual information:

    • User tag-cloud

    • Cooccurrence Network

    • User Cooccurrence Network

  • Can abstract this information as a vector of terms and weights (context)


Disambiguating flickr images

Disambiguating Flickr Images


Building better profiles

Building Better Profiles

  • What tags correspond to interests?

    • Locations and topics are useful, but other terms are not

  • TF / IDF Approach

    • It’s not that useful to find out we are all interested in HTML

  • Making use of the Category hierarchy

    • If I’m interested in Facebook, Flickr, Last.fm, Delicious, etc, I can extrapolate the interest Online_Social_Networks


Modelling users profiles and interests based on cross folksonomy analysis

http://tagora.ecs.soton.ac.uk/tag/apple

dbpedia:hasDbpediaSenseInfo

http://tagora.ecs.soton.ac.uk/tag/apple/sense-info/0

dbpedia:sense

dbpedia:senseWeight

0.30628910807

http://tagora.ecs.soton.ac.uk/dbpedia/resource/Apple_Inc.

owl:sameAs

dbpedia:hasTermFrequency

dbpedia:hasTermFrequencyPair

“mac”

_:b9510f00000000a5

35

dbpedia:hasTerm

http://tagora.ecs.soton.ac.uk/tag/apple/sense-info/1

dbpedia:sense

dbpedia:senseWeight

0.248912928

http://tagora.ecs.soton.ac.uk/dbpedia/resource/Apple

owl:sameAs

dbpedia:hasTermFrequency

dbpedia:hasTermFrequencyPair

“fruit”

_:b9510f00000000a5

41

dbpedia:hasTerm


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