Entity-Based Semantics Emerging from Personal Awareness Streams
-
Upload
amparo-elizabeth-cano -
Category
Technology
-
view
781 -
download
1
description
Transcript of Entity-Based Semantics Emerging from Personal Awareness Streams
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
Outline• Introduction• Related Work• Social Stream Aggregation and Entity-Based Concept Induction
– Modelling Context with Personal Awareness Streams– Methodology– Evaluation
• Conclusions
Outline
IntroductionIntroduction
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
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.
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.
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!!
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
IntroductionIntroduction
Can personal awareness streams convey meaningful information for modelling user context?
IntroductionIntroduction
Modelling User Context
People
Location Things
IntroductionIntroduction
Modelling User Context
-Semantic - Spatial
- Social - Temporal
Relationships:
IntroductionIntroduction
Modelling User Context what for ???
M-F
8:00 9:00 13:00 17:00- 20:00
S-S
IntroductionIntroduction
Modelling User Context what for ???
M-F
8:00 9:00 13:00 17:00- 20:00
S-S
BLT offer, 500m
IntroductionIntroduction
Modelling User Context what for ???
M-F
8:00 9:00 13:00 17:00- 20:00
S-S
BLT offer, 500m
Tuna
IntroductionIntroductionModelling User Context what for ???
M-F
8:00 9:00 13:00 17:00- 20:00
S-S
SuggestedBy a,b,c
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
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
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.
Social Stream Social Stream Aggregation and Entity Based Concept Induction
U q1 q1={authorship}
Defining a Tweetonomy
Social Stream Social Stream Aggregation and Entity Based Concept Induction
M q2
q2={direct message}
U q1 q1={author}
Defining a Tweetonomy
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}
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
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}
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
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
Social Stream Social Stream Aggregation and Entity Based Concept Induction
Modelling User Context with a Tweetonomy
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)
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
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
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
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?
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)
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;
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
Evaluation• Data Set
– Four active Microbloggers– From Jul - Sep 2010– From each message, entities where extracted using the
OpenCalais service.
Evaluation
EvaluationEvaluation
Concepts in the context of Hashtags-Places-Time
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
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
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.
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
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
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.
SlideshareSlideShare
http://www.slideshare.net/ampaeli/modellingContext