about the social semantic web

DelftUniversity ofTechnology

Social Semantic WebWhy we need semantics on the Social Web

Somewhere, Netherlands, September 27, 2011

Fabian AbelWeb Information Systems, TU Delft

2Social Semantic Web

The Social Web

Social Web stands for the culture of participation on the Web.


Power-law of participation by Ross Mayfield 2006


The Social Web

“Problem”The Social Web is made by people for

people


Why do we need semantics on the Social Web? (from an engineering point of view)

Social Web

Applications…that understand and

leverage Social Web data

user/usage data

Semantic Enrichment, Linkage and Alignment


Applications…that understand and

leverage Social Web data

About this talk

Social Web

user/usage data


1. Social tagging

2. Micro-blogging

Mapping words to ontological

concepts

User Modeling and Personalization


Social TaggingSemantics in Social Tagging Systems


Social Tagging Systems

• Folksonomy: • set of tag assignments• Formal model [Hotho et al. ‘07]:F = (U, T, R, Y)

baker, trumpet

armstrongdizzy, jazz

armstrongjazzmusic

trumpet

trumpetUsers

Tags

Resources

armstrong, baker, dizzy,

jazzmusic, jazz, trumpet

usertag

resource

tag assignment


Folksonomy Graph• A folksonomy (tag assignments) can be

represented via an undirected weighted tripartite graph GF = (VF, EF) where:• VF = U U T U R is the set of nodes• EF = {(u,t), (t,r), (u,r) | (u,t,r) in Y} is the set of edges


How to weigh the edges of a folksonomy graph?

• For example: • w(t,r) = {u in U| (u, t, r) in Y} = count the number of

users who assigned tag t to resource r

r1

u1

u2

t1

t2

r2

w(t1, r1)w(u1, t1)

w(u2, r2)

w(u,t) = ?

w(u,r) = ?

w(t,r) = ?

w(t1, r1) = ?

w(u1, t1) = ?

w(u2, r2) = ?

w(t1, r1) = 2

w(u1, t1) = 1

w(u2, r2) = 1

tag assignments: (u1, t1, r1), (u2, t1, r1), (u2, t2, r2)


FolkRank [Hotho et al. 2006] is an application of PageRank [Page et al. 98] for folksonomies:

0

0

1

0

0

0

u1

u2

t1

t2

r1

r2

FolkRank-based rankings: users tags resources

1.

2.

r1

u1

u2t1

t2 r2

Search & Ranking in Folksonomies

FolkRank vector preference vector

influence of preferencesadjacency matrix models the

folksonomy graph

r1u1

u2

t1

t2r2

u1 0.5 0.5

u2 0.25 0.25 0.25 0.25

t1 0.25 0.25 0.5

t2 0.5 0.5

r1 0.25 0.25 0.5

r2 0.5 0.5

u1 u2 t1 t2 r1 r20.1

0.2

0.3

0.1

0.3

0.1

u1

u2

t1

t2

r1

r2

r1u1 t1

A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Information retrieval in folksonomies: Search and ranking. In Proc. ESWC, volume 4011 of LNCS, pages 411–426, Budva, Montenegro, 2006. Springer.


Problems of traditional folksonomies

baker, trumpet

armstrongdizzy, jazz

armstrongjazzmusic

trumpet

trumpetTags

armstrong, baker, dizzy,

jazzmusic, jazz, trumpet

no tags

ambiguityof tagssynonyms


“Metadata” in Folksonomies

• Metadata-enabled folksonomy:Fc = (U, T, R, Y, M, Z)

- M is the actual metadata information- Z Y x M is the set of metadata assignments

usertag

resource

tag assignment

metadata

User XAge: 30 yearsEducation: …

metadata

Jazz (noun) is a style of music that…

music

jazz

metadataResource Ycreated: 1979-12-06creator: …

metadata

User Xjazz

TAS XYcreated: 2011-04-19meaning: dbpedia:Jazz


Exploiting Metadata in Folksonomies

jazz

jazzmusic

FolkRank’s

adjacency matrix:

… jazz jazzmusic ... ...r1 1r2 1...

Using FolkRank to search for resources related to jazz:

r1

r2

… dbpedia:Jazz ... ...r1 1r2 1...

meaning:dbpedia:Jazz

meaning:dbpedia:Jazz

DBpedia-based FolkRank can

improve search performance, e.g. for Flickr images

ESWC ‘10

http://www.springerlink.com/content/q4801u674n722221/


Representing Tagging Activities in RDF

<http://example.org/tas/1>

a tag:RestrictedTagging;

tag:taggedResource <http://example.org/23.png>;

foaf:maker <http://fabianabel.myopenid.com>;

tag:associatedTag <http://example.org/tag/armstrong>;

.

http://example.org/23.png

fabian

armstrong

Representation of tag assignment via Tag Ontology:

Tag ontology: http://www.holygoat.co.uk/projects/tags/ MOAT: http://moat-project.org/

moat:tagMeaning <http://dbpedia.org/resource/Louis_Armstrong>

& MOAT extension

http://www.holygoat.co.uk/projects/tags/

http://moat-project.org/


Pointers• RDF vocabularies: • Tag ontology: http://www.holygoat.co.uk/projects/tags/ • MOAT: http://moat-project.org/ • SCOT: http://www.scot-project.org/

• Tagging datasets: http://kmi.tugraz.at/staff/markus/datasets/

• ICWSM ‘10 Tutorial on Social Semantic Web: http://www.slideshare.net/Cloud/the-social-semantic-web

• NER tools: DBpedia spotlight, Alchemey, OpenCalais, Zemanta,…

• Papers:• Folksonomy Model and FolkRank: Hotho et al.: Information retrieval

in folksonomies: Search and ranking. ESWC 2006. • MOAT framework: A. Passant: Meaning Of A Tag: A collaborative

approach to bridge the gap between tagging and Linked Data. LDOW 2008.

• Learning semantics from social tagging: • Marinho et al.: Folksonomy-based Collabulary Learning. ISWC 2008.

• Hotho et al.: Emergent Semantics in BibSonomy. LNI vol. 94, 2006.

• P. Mika: Ontologies are us: A unified model of social networks and semantics.

Web Semantics vol. 5(1), 2007.

http://www.holygoat.co.uk/projects/tags/

http://moat-project.org/

http://www.scot-project.org/

http://kmi.tugraz.at/staff/markus/datasets/

http://www.slideshare.net/Cloud/the-social-semantic-web


Micro-bloggingMaking sense of micro-blogging data


Challenge: inferring interests from tweets

I want my personalized

news recommendatio

ns!Analysis and User Modeling


Personalized News Recommender

Profile

?

(How) can we infer a Twitter-based user profile that

supports the news recommender?


1. What type of concepts should represent “interests”?

Profile?concept weight

1. Profile Type

Francesca Schiavone won French Open #fo2010 ?

Francesca Schiavone

FrenchOpen

Francesca Schiavone French Open entity-

based

SportT

T topic-based

# fo2010

#fo2010# hashtag-

based

time

June 27 July 4 July 11


Performance of User Modeling strategies

Entity-based strategy improves the recommendation quality significantly (MRR & S@10)

Topic-based strategy improves S@10 significantly

T

#

Profile Type


2. Which tweets of the user should be analyzed?

Profile?concept weight

?

timeMorning:Afternoon:Night:

2. Temporal

Constraints


(b) temporal patterns

weekendsstart

end

(a) time period


Temporal patterns of user profiles

topic-based user profiles

weekday vs. weekend profilesd1(pweekday, pweekend)

day vs. night profilesd1(pday, pnight)

1. Weekend profiles differ significantly from weekday profiles

2. the difference is stronger than between day and night profiles

2

Temporal Constraint

s


Impact of temporal constraints

Selection of temporal constraints depends on type of

user profile.

•Topic-based profiles: adapting to temporal context is beneficial• Entity-based profiles: long-term profiles perform better

Adapting to temporal context helps?

yes

no

yes

no

T

T

time

startcomplet

eend

complete: 2 months

Recommendations = ?

startfresh

fresh: 2 weeks

time

start end

Recommendations = ?

weekends

Temporal Constraint

s


3. Further enrich the semantics of tweets?

Profile?concept weightFrancesca Schiavone

won! http://bit.ly/2f4t7a

Francesca Schiavone

3. Semantic

Enrichment

Francesca Schiavone

Francesca wins French Open

Thirty in women'stennis is primordially old, an age when agility and desire recedes as the …

French Open

Tennis

French OpenTennis

(b) further enrichment

(a) tweet-based


Tweet-based

further enrichment(e.g. exploiting links)

topic-based user profiles

More distinct entities per profile

further enrichment(e.g. exploiting links)

Tweet-based

entity-based user profiles

Impact of Semantic Enrichment

Exploiting external resources allows for significantly richer user profiles (quantitatively)

More distinct topics per profile

3. Semantic

Enrichment


Impact of Semantic Enrichment

Tweet-based

Further enrichment

Further semantic enrichment (exploiting links) improves the quality of the Twitter-based profiles!

T

3. Semantic

Enrichment


How to weights the concepts?

Profile? concept weightFrancesca

Schiavone

French OpenTennis

time


?

weight(Francesca Schiavone)

Based on concept occurrence frequency (CF)

4

weight(French Open)

weight(Tennis)

36

CF

CF*IDF

Time Sensitive

4. Weighting Scheme


Impact of weighting scheme4.

Weighting Scheme

Time-sensitive weighting functions perform best (for news recommendations)

time sensitivenot time sensitive


Observations

1. Profile type:• Semantic profiles (entity-based and topic-based) perform

better than hashtag-based profiles

2. Temporal Constraints: • Adapting to temporal context (e.g. weekend patterns)

makes sense if it does not cause sparsity problems

3. Semantic Enrichment:• Further semantic enrichment improves

profile/recommendation quality

4. Weighting Scheme:• Time-sensitive weighting functions allow for best news

recommendation performance


Pointers

• Related papers, datasets & code: http://wis.ewi.tudelft.nl/tweetum/

• ESWC 2011 workshop on “Making Sense of Microposts”: http://research.hypios.com/msm2011/

• Special Issue at Semantic Web Journal: http://www.semantic-web-journal.net/blog/special-issue-semantics-microposts (deadline: Nov 15)

http://wis.ewi.tudelft.nl/tweetum/

http://research.hypios.com/msm2011/

http://www.semantic-web-journal.net/blog/special-issue-semantics-microposts

http://www.semantic-web-journal.net/blog/special-issue-semantics-microposts


Linking Social DataCross-system User Modeling


Pitfalls of today’s Web Systems

System A

time

Hi, I’m your new user. Give me

personalization!

Hi, I have a new-user problem!

profile ?

profile

Hi, I don’t know that your

interests changed!

Hi, I’m back andI have new interests.

System C

profile

System D

profile

System B

profile

How can we tackle these problems?


User data on the Social Web

Cross-system user modeling on the Social Web


Google Profile URI http://google.com/profile/XY

4. enrich data withsemantics

WordNet®

Semantic Enhancement

Profile Alignment

3. Map profiles totarget user model

FOAF vCard

Blog posts:

Bookmarks:

Other media:

Social networking profiles:

2. aggregate public profile

data

Social Web Aggregator

1. get other accounts of user

SocialGraph API

Account Mapping

Aggregated, enriched profile(e.g., in RDF or vCard)

Analysis and user modeling

5. generate user profiles

Interweaving public user data


Analysis: form-based profiles

2. Benefits of Profile Aggregation:a. more profile attributesb. more complete profiles

338 users with filled form-based profiles at the five different services.


Overlap of tag-based profiles

Overlap of tag-based profiles is less than 10% for more than 90% of the users


Cold-start: Recommending tags / bookmarks

How does cross-system user modeling impact the recommendation quality (in cold-start situations)?

Hi, I’m your new user. Give me

personalization!

profile

?

delicious

Cosi

ne-b

ase

dre

com

mend

er

tags to explore

Web sites to bookmark

profile

profile

Cro

ss-s

yst

em

use

r m

od

elin

g

leave-n-out evaluation

user’s tags and bookmarks

Ground truth:

actual tags and bookmarks of the user


Bookmark Recommendations

baselineCross UM Cross UM

1. Cross-system user modeling achieves significant improvements for cold-start bookmark recommendations

2. Twitter is a more appropriate source than Flickr


Tag Recommendations over time

Consideration of external profile information

(Mypes) also leads to significant improvement when the profiles in the target

service are growing.

Baseline (target profile)


Observations

• Aggregating Social Profile Data leads to tremendous (and significant) improvements of tag and bookmark recommendation quality in cold-start situations and beyond

• To optimize the performance one has to adapt the cross-system strategies to the concrete application setting


Pointers

• Workshop series on “Social Data on the Web”: http://sdow.semanticweb.org/

• RDF vocabularies:• SIOC: http://rdfs.org/sioc/spec/ • FOAF: http://xmlns.com/foaf/spec/ • Weighted Interest Vocabulary:

http://purl.org/ontology/wi/core# • Papers:

• Abel et al.: Cross-system User Modeling and Personalization on the Social Web. UMUAI (to appear 2011) http://wis.ewi.tudelft.nl/papers/2011-umuai-cross-system-um.pdf

• B. Mehta. Cross System Personalization: Enabling personalization across multiple systems. PhD thesis, 2009.

http://sdow.semanticweb.org/

http://rdfs.org/sioc/spec/

http://xmlns.com/foaf/spec/

http://purl.org/ontology/wi/core

http://wis.ewi.tudelft.nl/papers/2011-umuai-cross-system-um.pdf

http://wis.ewi.tudelft.nl/papers/2011-umuai-cross-system-um.pdf


2 Take-away QuestionsPossible Future Work


What kind of knowledge can we learn from users’ tagging and micro-blogging activities?

r1u1

u2

t1

t2r2


How can we find “information” in social (micro-)streams?

Answer

Question

translate between query and Twitter vocabulary

compose answer

see also TREC Microblogging Task: http://trec.nist.gov/data/tweets/

http://trec.nist.gov/data/tweets/


Thank you!

Twitter: @fabianabelhttp://persweb.org/