entity-based semantics emerging from personal awareness streams
DESCRIPTION
TRANSCRIPT
![Page 1: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/1.jpg)
Capturing Entity-Based Semantics Emerging from Personal Awareness Streams
A.E. Cano, S.Tucker, F. CiravegnaThe Oak Group,
Department of Computer Science, The University of Sheffield
![Page 2: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/2.jpg)
Outline• Introduction• Related Work• Social Stream Aggregation and Entity-Based Concept Induction
– Modelling Context with Personal Awareness Streams– Methodology– Evaluation
• Conclusions
Outline
![Page 3: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/3.jpg)
IntroductionIntroduction
![Page 4: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/4.jpg)
IntroductionIntroduction
Social Awareness Streams
[1] M. Naaman, J. Boase, and C. H. Lai. Is it really about me?: message content in social awareness streams. In CSCW ’10: Proceedings of the 2010 ACM conference on Computer supported cooperative work, pages 189–192, New York, NY, USA, 2010. ACM.
Collection of semi-public, natural language message produced by different users and characterised by their brevity
![Page 5: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/5.jpg)
IntroductionIntroductionSocial Awareness Streams
[1] M. Naaman, J. Boase, and C. H. Lai. Is it really about me?: message content in social awareness streams. In CSCW ’10: Proceedings of the 2010 ACM conference on Computer supported cooperative work, pages 189–192, New York, NY, USA, 2010. ACM.
![Page 6: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/6.jpg)
IntroductionIntroductionSocial Awareness Streams
[1] M. Naaman, J. Boase, and C. H. Lai. Is it really about me?: message content in social awareness streams. In CSCW ’10: Proceedings of the 2010 ACM conference on Computer supported cooperative work, pages 189–192, New York, NY, USA, 2010. ACM.
![Page 7: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/7.jpg)
IntroductionIntroductionSocial Awareness Streams
[1] M. Naaman, J. Boase, and C. H. Lai. Is it really about me?: message content in social awareness streams. In CSCW ’10: Proceedings of the 2010 ACM conference on Computer supported cooperative work, pages 189–192, New York, NY, USA, 2010. ACM.
People talk a lot about themselves!!
![Page 8: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/8.jpg)
IntroductionIntroduction
Personal Awareness Streams
[2] C. Wagner and M. Strohmaier. The wisdom in tweetonomies: Acquiring latent conceptual structures from social awareness streams. In Proc. of the Semantic Search 2010 Workshop (SemSearch2010), april 2010..
Collection of semi-public, natural language message produced by a user and characterised by their brevity
![Page 9: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/9.jpg)
IntroductionIntroduction
Can personal awareness streams convey meaningful information for modelling user context?
![Page 10: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/10.jpg)
IntroductionIntroduction
Modelling User Context
People
Location Things
![Page 11: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/11.jpg)
IntroductionIntroduction
Modelling User Context
-Semantic - Spatial
- Social - Temporal
Relationships:
![Page 12: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/12.jpg)
IntroductionIntroduction
Modelling User Context what for ???
M-F
8:00 9:00 13:00 17:00- 20:00
S-S
![Page 13: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/13.jpg)
IntroductionIntroduction
Modelling User Context what for ???
M-F
8:00 9:00 13:00 17:00- 20:00
S-S
BLT offer, 500m
![Page 14: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/14.jpg)
IntroductionIntroduction
Modelling User Context what for ???
M-F
8:00 9:00 13:00 17:00- 20:00
S-S
BLT offer, 500m
Tuna
![Page 15: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/15.jpg)
IntroductionIntroductionModelling User Context what for ???
M-F
8:00 9:00 13:00 17:00- 20:00
S-S
SuggestedBy a,b,c
![Page 16: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/16.jpg)
Related WorkRelated Work
• Java et al [ 3], present an analysis of Twitter which suggest that the differences in users’ network connection structures can be explained by the following types of user activities: information seeking, information sharing and social activity.
• Ramage et al [4], apply labelled Latent Dirichlet Allocation (LDA) for mapping content of the public Twitter feed into four dimensions including style and substance.
• Krishnamurthy et al [5] present a characterisation of Twitter social network, which includes patterns in geographic growth and user’s social activity.
Social Awareness Streams
![Page 17: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/17.jpg)
Related WorkRelated Work
• Wagner and Strohmaier [2] introduce the Tweetonomy model- Formalisation of social awareness streams.- Based on lightweight associative ontologies.
• Stankovic et al [6], study conference related tweets. - Map tweets to talks an sub-events that they refer to.- Using linked data they derive additional knowledge about event
dynamics and user activities.
Social Awareness Streams Using Linked Data
![Page 18: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/18.jpg)
Related WorkRelated WorkOur work differs from existing work in …
• Focus on deriving person-based lightweight ontologies from personal awareness stream; which enrich concepts and reveal structures that are meaningful to the owner of the stream.
• Analyse the content of the messages not only in terms of traditional resources as hashtags, and links, but also in terms of entities (e.g location, people, organisations and time).
•Present a methodology based on tensor analysis that allows the definition of entity-based context for deriving person-based ontologies.
![Page 19: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/19.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
U q1 q1={authorship}
Defining a Tweetonomy
![Page 20: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/20.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
M q2
q2={direct message}
U q1 q1={author}
Defining a Tweetonomy
![Page 21: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/21.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
M q2
q2={direct message}
U q1 q1={author}
R q3
Defining a Tweetonomy
q3={Links, Hash tags, Location, People,Places, Organisation}
![Page 22: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/22.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
M q2
q2={direct message}
U q1 q1={author}
T
q3
q3={Links, Hash tags, Location, People,Places, Organisation}
R
T U×M×R⊆
Defining a Tweetonomy
![Page 23: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/23.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
M q2
q2={direct message}
U q1 q1={author}
Defining a Tweetonomy
T
q3R
T U×M×R⊆
Function that assigns a temporal marker to each ternary edge.
ft
q3={Links, Hash tags, Location, People,Places, Organisation}
![Page 24: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/24.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
M q2
q2={direct message}
U q1 q1={author}
T
q3
q3={Links, Hash tags}
R
T U×M×R⊆
Function that assigns a temporal marker to each ternary edge.
ft
Tweetonomy
S={Uq1, Mq2, Rq3, T, ft}
Defining a Tweetonomy
![Page 25: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/25.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a Tweetonomy
Location People Keyword
Sheffield @gigsandtours, @officialcallumw
Tickets, centre,visit, retail, destination
Leeds @gigsandtours, @officialcallumw
Tickets, centre,visit, retail, destination
![Page 26: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/26.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a Tweetonomy
![Page 27: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/27.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a Tweetonomy
@Johbinns @tony
therapy 0.045 0
alcohol 0.034 0
fan 0.012 0
work 0 0.08
Okp =(RkM)(MRp)
![Page 28: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/28.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a Tweetonomy
@Johnbinns @tony
therapy 0.045 0
alcohol 0.034 0
fan 0.012 0
work 0 0.08
Okp =(RkM)(MRp)
@Johbinnstherapy
alcohol
fan
@Tony
work
![Page 29: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/29.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a Tweetonomy
Sheffield Leeds
therapy 0.045 0.023
alcohol 0.034 0.012
fan 0.012 0
work 0.056 0
Okl =(RkM)(MRl)
Leedstherapy
alcohol
fan
Sheffield
work
work
alcohol
![Page 30: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/30.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a Tweetonomy
Morning (7am-12:00pm)
Rest of the Day(12:00pm-6:59am)
therapy 0.0015 0.023
alcohol 0 0.062
fan 0.0012 0.03
work 0.066 0
Otl =(RtM)(MRt)
Morning therapy
fan
Rest of the Day
work
work
alcohol
![Page 31: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/31.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a TweetonomyWhat are the concepts that emerge when analysing BigGayShaun in the context of Sheffield (Location), @Johnbinns (Person), during the evening?
![Page 32: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/32.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a TweetonomyWhat are the concepts that emerge when analysing BigGayShaun in the context of Sheffield (Location), @Johnbinns (Person), during the evening?
Morning (7am-12:00pm)
Rest of the Day(12:00pm-6:59am)
therapy 0.0015 0.023
alcohol 0 0.062
fan 0.0012 0.03
work 0.066 0
Otl =(RtM)(MRt)
@Johnbinns @tony
therapy 0.045 0
alcohol 0.034 0
fan 0.012 0
work 0 0.08
Okp =(RkM)(MRp)
Sheffield Leeds
therapy 0.045 0.023
alcohol 0.034 0.012
fan 0.012 0
work 0.056 0
Okl =(RkM)(MRl)
![Page 33: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/33.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a Tweetonomy
Given P lightweight ontologies characterising a user’s social streams consisting of N messages; we define a tensor O ∈RN×N×P consisting of frontal slices of the form Op=Bp BT
p with p=1, ..P ,where B is a bipartite ontology Op;
![Page 34: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/34.jpg)
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a TweetonomyGiven P lightweight ontologies characterising a user’s social streams consisting of N messages; we define a tensor O R∈ N×N×P consisting of frontal slices of the form Op=Bp BT
p
with p=1, ..P ,where B is a bipartite ontology Op;
What are the concepts that emerge when analysing BigGayShaun in the context of Sheffield (Location (1)), @Johnbinns (Person (2)), during the evening (Time (3))?
therapy alcohol fan work
therapy 0.002 0 .. ..
alcohol .. 0.0011 .. ..
fan .. … 0.0001 ..
work … .. … 0.0004
O(1) =Okl(Okl)T
therapy alcohol fan work
therapy 0.002 0 .. ..
alcohol .. 0.0011 .. ..
fan .. … 0.0001 ..
work … .. … 0.0004
O(2) =Okp(Okp)T
therapy alcohol fan work
therapy 0.002 0 .. ..
alcohol .. 0.0011 .. ..
fan .. … 0.0001 ..
work … .. … 0.0004
O(3) =Okt(Okt)T
![Page 35: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/35.jpg)
Evaluation• Data Set
– Four active Microbloggers– From Jul - Sep 2010– From each message, entities where extracted using the
OpenCalais service.
Evaluation
![Page 36: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/36.jpg)
EvaluationEvaluation
Concepts in the context of Hashtags-Places-Time
![Page 37: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/37.jpg)
Evaluation• Data Set
– Four active Microbloggers– From Jul - Sep 2010– From each message, entities where extracted using the
OpenCalais service.• User-based evaluation:
Consulting the author of the social stream whose context-induced concepts are being mapped.
Evaluation
![Page 38: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/38.jpg)
Evaluation• Data Set
– Four active Microbloggers– From Jul - Sep 2010– From each message, entities where extracted using the
OpenCalais service.• User-based evaluation:
Consulting the author of the social stream whose context-induced concepts are being mapped.
• Evaluated contexts : hashtag-time, location-people, and organisation-people
Evaluation
![Page 39: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/39.jpg)
EvaluationEvaluation
Higher lexical diversity (K/M) leads to better MAP results (see Figure 3 b)), this is an expected result since CSISSA explores the way in which an entityis linked to another one through keywords.
![Page 40: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/40.jpg)
EvaluationHighlights
- Users tend to forget what they’ve tweeted about.
- Entity relationships decay with time. - Users’ streaming topics’ relevance was in many cases volatile;further research is necessary to address these issues
![Page 41: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/41.jpg)
Conclusions• Awareness streams can be used to model context by
leveraging the user’s entity affiliations.• In our experiments a fairly naive approach was taken by not
considering the ambiguity in which user’s can relate two entities with a keyword.
• Future work considers:– Introduction of concept disambiguation for tackling this issue.– Use this approach for merging user contexts in pervasive
environments.
Conclusions
![Page 42: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/42.jpg)
ReferencesReferences[1] M. Naaman, J. Boase, and C. H. Lai. Is it really about me?: message content in social awareness streams. In CSCW ’10: Proceedings of the 2010 ACM conference on Computer supported cooperative work, pages 189–192, New York, NY, USA, 2010. ACM.[2] C. Wagner and M. Strohmaier. The wisdom in tweetonomies: Acquiring latent conceptual structures from social awareness streamshmaier.. In Proc. of the Semantic Search 2010 Workshop (SemSearch2010), april 2010..
[3] A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usageand communities. In WebKDD/SNA-KDD ’07: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56–65, New York, NY, USA, 2007. ACM.
[5] B.Krishnamurthy, P.Gill, and M.Arlitt. A few chirps about twitter. In WOSP’08: Proceedings of the first workshop on Online social networks, pages 19–24, New York, NY, USA, 2008.ACM.
[4] D. Ramage, D. Hall, R. Nallapati, and C. D. Manning. Labeled lda: a supervised topicmodel for credit attribution in multi-labeled corpora. In EMNLP ’09: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 248–256, Morristown, NJ, USA, 2009. Association for Computational Linguistics.
[6] M. R. M. Stankovic and P. Laublet. Mapping tweets to conference talks: A goldmine for semantics. In Proceedings of Social Data on the Web workshop, ISWC 2010. Shanghai, China. ISWC 2010, 2010.
![Page 43: Entity-Based Semantics Emerging from Personal Awareness Streams](https://reader035.vdocument.in/reader035/viewer/2022062616/5496e7fcac79591d2e8b5191/html5/thumbnails/43.jpg)
SlideshareSlideShare
http://www.slideshare.net/ampaeli/modellingContext