about the social semantic web
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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.
3Social Semantic Web
Power-law of participation by Ross Mayfield 2006
4Social Semantic Web
The Social Web
“Problem”The Social Web is made by people for
people
5Social Semantic Web
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
6Social Semantic Web
Applications…that understand and
leverage Social Web data
About this talk
Social Web
user/usage data
Semantic Enrichment, Linkage and Alignment
1. Social tagging
2. Micro-blogging
Mapping words to ontological
concepts
User Modeling and Personalization
7Social Semantic Web
Social TaggingSemantics in Social Tagging Systems
8Social Semantic Web
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
9Social Semantic Web
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
10Social Semantic Web
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)
11Social Semantic Web
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.
12Social Semantic Web
Problems of traditional folksonomies
baker, trumpet
armstrongdizzy, jazz
armstrongjazzmusic
trumpet
trumpetTags
armstrong, baker, dizzy,
jazzmusic, jazz, trumpet
no tags
ambiguityof tagssynonyms
13Social Semantic Web
“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
14Social Semantic Web
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
15Social Semantic Web
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
16Social Semantic Web
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.
17Social Semantic Web
Micro-bloggingMaking sense of micro-blogging data
18Social Semantic Web
Challenge: inferring interests from tweets
I want my personalized
news recommendatio
ns!Analysis and User Modeling
Semantic Enrichment, Linkage and Alignment
Personalized News Recommender
Profile
?
(How) can we infer a Twitter-based user profile that
supports the news recommender?
19Social Semantic Web
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
20Social Semantic Web
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
21Social Semantic Web
2. Which tweets of the user should be analyzed?
Profile?concept weight
?
timeMorning:Afternoon:Night:
2. Temporal
Constraints
June 27 July 4 July 11
(b) temporal patterns
weekendsstart
end
(a) time period
22Social Semantic Web
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
23Social Semantic Web
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
24Social Semantic Web
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
25Social Semantic Web
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
26Social Semantic Web
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
27Social Semantic Web
How to weights the concepts?
Profile? concept weightFrancesca
Schiavone
French OpenTennis
time
June 27 July 4 July 11
?
weight(Francesca Schiavone)
Based on concept occurrence frequency (CF)
4
weight(French Open)
weight(Tennis)
36
CF
CF*IDF
Time Sensitive
4. Weighting Scheme
28Social Semantic Web
Impact of weighting scheme4.
Weighting Scheme
Time-sensitive weighting functions perform best (for news recommendations)
time sensitivenot time sensitive
29Social Semantic Web
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
30Social Semantic Web
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)
31Social Semantic Web
Linking Social DataCross-system User Modeling
32Social Semantic Web
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?
33Social Semantic Web
User data on the Social Web
Cross-system user modeling on the Social Web
34Social Semantic 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
35Social Semantic Web
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.
36Social Semantic Web
Overlap of tag-based profiles
Overlap of tag-based profiles is less than 10% for more than 90% of the users
37Social Semantic Web
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
38Social Semantic Web
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
39Social Semantic Web
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)
40Social Semantic Web
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
41Social Semantic Web
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.
42Social Semantic Web
2 Take-away QuestionsPossible Future Work
43Social Semantic Web
What kind of knowledge can we learn from users’ tagging and micro-blogging activities?
r1u1
u2
t1
t2r2
44Social Semantic Web
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/
45Social Semantic Web
Thank you!
Twitter: @fabianabelhttp://persweb.org/
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