shareit: mining socialmedia activities for detecting events
DESCRIPTION
ShareIt: Mining #SocialMedia Activities for Detecting #Events, Talk given at the 2nd Summer School on Social Media Retrieval (S3MR), June 2011, Antalya, TurkeyTRANSCRIPT
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Cover of the December 25, 2006
issue
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Quiz Test : who has already ...
1. edited a Wikipedia page?
2. shared photos on Flickr / Picassa?
3. uploaded a video on YouTube / Dailymotion?
4. used a mobile-aware application: Foursquare / Gowalla?
5. published a thought / comment on a blog?
6. published its status on Twitter / Identi.ca / FriendFeed?
7. shared bookmarks on Del.ico.us / Faviki?
8. own a Facebook account and does all this?
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What do you do for getting event info?
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This official event page does a very poor job to bring structured information(Sander Koelstra)
http://s3mr.eu/agenda/
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Looking for more structured information?
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Looking for some media?
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Looking for some media?
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Anything on Flickr / YouTube?
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Video Lectures reports 1 event and 3 lectures
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SSMS participants were better “sharer”
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Looking for some live information?
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Not that much of activity on Twitter
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Facebook is the place to be, right?
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http://www.flickr.com/photos/crsan/3697785107
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We have directory of events...
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There’s a lot of information out there…
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http://www.flickr.com/photos/mwparenteau/432039783 26
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1. Discover PAST, PRESENT and FUTURE events2. Live, relive and predict experiences through shared media3. Identify meaningful and/or interesting relationships
between events/media/people
EventMedia Goals
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Agenda
A crash course in the world of structured data#microdata , #microformat , #rdfa#rdf , #owl , #skos , #sparql , #linkeddata
EventMedia (User-centered design approach)LODE: a model for representing eventsScraping and interlinking description of eventsEnriching events with illustrating mediaDetecting events from social media activities
Detecting events from human sensing#twitter , #foursquare , #facebook
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Fundamental shift:
From sending bits from one host to the other towards making sense of those bits
From the Web to the Web of Data
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BelgianChocolates.com
Pralinés Deluxe Mix2,99€/100g Shopping Cart
From the Web to the Web of Data
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BelgianChocolates.com
Pralinés Deluxe Mix2,99€/100g Shopping Cart
Merchant Name
Product Name
Price
Product Image
From the Web to the Web of Data
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From the Web to the Web of Data
How can website owners help Google make sense of their bits?
Mark up their content using any of the following syntaxes:MicrodataMicro formatRDFa
"[...] We realized that structured data on the Web can and should accommodate multiple encodings."
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for specific, common, concise data
for custom data, RDF data, multiple schemas
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your dataRDFa = a domain-independent way to explicitly
embed
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RDF dataRDFa = a domain-independent way to explicitly
embed
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RDFa stands for…
RDF… in HTML … attributes
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RDFa in attributes of a web page to…
… transfer data from an application to another through the web.
… write data only once for web users and web applications.
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weaving RDFain web pages
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RDFa step 1declare the schemas you are using
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RDFa step 2use attributes to mark, type and add data
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RDFa step 3let RDFa agents extract RDF from the document
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web pagetake this minimal
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web pagedon't look at the code of this<html xmlns="http://www.w3.org/1999/xhtml" xmlns:cal="http://www.w3.org/2002/12/cal/icaltzd#" xmlns:xs="http://www.w3.org/2001/XMLSchema#" > <body><p about="#event1" instanceof="cal:Vevent">
<b property="cal:summary">Weekend off in Iona</b>: <span property="cal:dtstart" datatype="xs:date">2006-10-21
</span> to<span property="cal:dtend" datatype="xs:date">2006-10-23
</span>.see <a rel="cal:url" href="http://freetime.example.org/">Free time web site</a> for info on<span property="cal:location">Iona, UK</span>.
</p></body></html>
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web pageschemas for data in this<html xmlns="http://www.w3.org/1999/xhtml" xmlns:cal="http://www.w3.org/2002/12/cal/icaltzd#" xmlns:xs="http://www.w3.org/2001/XMLSchema#" > <body><p about="#event1" instanceof="cal:Vevent">
<b property="cal:summary">Weekend off in Iona</b>: <span property="cal:dtstart" datatype="xs:date">2006-10-21
</span> to<span property="cal:dtend" datatype="xs:date">2006-10-23
</span>.see <a rel="cal:url" href="http://freetime.example.org/">Free time web site</a> for info on<span property="cal:location">Iona, UK</span>.
</p></body></html>
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web pagedata seen by users viewing this<html xmlns="http://www.w3.org/1999/xhtml" xmlns:cal="http://www.w3.org/2002/12/cal/icaltzd#" xmlns:xs="http://www.w3.org/2001/XMLSchema#" > <body><p about="#event1" instanceof="cal:Vevent">
<b property="cal:summary">Weekend off in Iona</b>:<span property="cal:dtstart" datatype="xs:date">2006-10-21
</span> to<span property="cal:dtend" datatype="xs:date">2006-10-23
</span>.see <a rel="cal:url" href="http://freetime.example.org/">Free time web site</a> for info on<span property="cal:location">Iona, UK</span>.
</p></body></html>
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web pagedata for an RDFa agent in this<html xmlns="http://www.w3.org/1999/xhtml" xmlns:cal="http://www.w3.org/2002/12/cal/icaltzd#" xmlns:xs="http://www.w3.org/2001/XMLSchema#" > <body><p about="#event1" instanceof="cal:Vevent">
<b property="cal:summary">Weekend off in Iona</b>: <span property="cal:dtstart" datatype="xs:date">2006-10-21
</span> to<span property="cal:dtend" datatype="xs:date">2006-10-23
</span>.see <a rel="cal:url" href="http://freetime.example.org/">Free time web site</a> for info on<span property="cal:location">Iona, UK</span>.
</p></body></html>
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web pagedata shared by both in this<html xmlns="http://www.w3.org/1999/xhtml" xmlns:cal="http://www.w3.org/2002/12/cal/icaltzd#" xmlns:xs="http://www.w3.org/2001/XMLSchema#" > <body><p about="#event1" instanceof="cal:Vevent">
<b property="cal:summary">Weekend off in Iona</b>: <span property="cal:dtstart" datatype="xs:date">2006-10-21
</span> to<span property="cal:dtend" datatype="xs:date">2006-10-23
</span>.see <a rel="cal:url" href="http://freetime.example.org/">Free time web site</a> for info on<span property="cal:location">Iona, UK</span>.
</p></body></html>
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web pagewhat an RDFa agent knows
from this
#event1 isA cal:Vevent
#event1 cal:summary "Weekend off in Iona"
#event1 cal:dtstart "2006-10-21"^^xs:date
#event1 cal:dtend "2006-10-23"^^ xs:date
#event1 cal:url <http://freetime.example.org/>
#event1 cal:location "Iona, UK"
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RDFis the first layer of the Semantic
Web standards
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RDFstands for
Resource Description Framework
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RDF
is a triple model i.e. every piece of knowledge is broken down into
( subject , predicate , object )
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image.jpg has for creator Raphael and depicts the elephant Ganesh
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image.jpg has for creator Raphaelimage.jpg depicts the elephant Ganesh
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( image.jpg , creator , Raphael )( image.jpg , depicts , Elephant Ganesh )
( subject , predicate , object )
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in the atoms of knowledge are triples of the form
(subject,predicate,object)
RDF
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triples can be seen as arcsof a graph (vertex,edge,vertex)RDF
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image.jpg
Ganesh
Raphael
creator
depicts
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in resources and properties are identified by URIs
RDF
http://mydomain.org/mypath/myresource
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in values of properties can also be literals i.e. strings of characters
RDF
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http://flickr.com/photos/rtroncy/2923/
Elephant Ganesh
http://www.cwi.nl/~troncy#me
http://purl.org/dc/elements/1.1#creator
http://xmlns.com/foaf/0.1#depicts
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The RDF Data Model
An RDF document is an unordered collection of statements, each with a subject, predicate and object (aka triples)
A triple can be thought of as a labelled arc in a graph
Statements describe properties of web resources
A resource is any object that can be pointed to by a URI: a document, a picture, a paragraph on the Web, etc.
Properties themselves are also resources (URIs)
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Example of RDF Graphs
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Simple example (Google Vocab)<div xmlns:v="http://rdf.data-vocabulary.org/#" typeof="v:Event">
<a href=http://www.example.com/events/poisel_offenback.hmtlrel="v:url" property="v:summary">Philipp Poisel in Offenbach</a>
<span property="v:description">See Philipp Poisel in Offenbach</span>When: <span property="v:startDate" content="2011-01-16T19:00-01:00">Jan 16, 7:00PM</span><span property="v:endDate" content="2011-01-16T21:00-01:00">9:00PM</span>Where: <span rel="v:location"><span typeof="v:Organization">
<span property="v:name">Capitol</span>,<span rel="v:address"><span typeof="v:Address">
<span property="v:street-address">Kaiserstrae 106</span>,<span property="v:locality">Offenbach am Main</span>,
</span></span><span rel="v:geo"><span typeof="v:Geo">
<span property="v:latitude" content="50.10945"></span><span property="v:longitude" content="8.76579" ></span>
</span></span></span></span>Category: <span property="v:eventType">Concert</span>
</div>
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Rich Snippet Preview
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Rich Snippet Preview for Reviews
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Rich Snippet Preview for People
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Rich Snippet Preview for Recipes
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Rich Snippet Preview for Events
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Yahoo! Enhanced Results
Enhanced result with deep links, rating, address.
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Yahoo! Vertical Intent Search
Related actors and movies
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Snippet generation using metadata
Yahoo displays enriched search results for pages that contain microformat or RDFa markup using recognized ontologies Displaying data, images, video Example: GoodRelations for products Enhanced results also appear for sites from which we extract information
ourselves
Also used for generating facets that can be used to restrict search results by object type Example: “Shopping sites” facet for products
Documentation and validator for developers http://developer.search.yahoo.com
Formerly: SearchMonkey allowed developers to customize the result presentation and create new ones for any object type
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How search engines get this data?
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Behind the scene With RDFa markup:
<div xmlns:v=http://rdf.data-vocabulary.org/#typeof="v:Review-aggregate">
<span rel=“v:itemreviewed"><h1 property="v:name">Drooling Dog Bar B Q</h1><img rel="v:rating" src="stars_map.png" alt="4 star
rating"/><em>based on<span property="v:count">15</span> reviews</em></span>
</div>
With Micro-format markup:<div class="hreview-aggregate"><span class="item vcard"><h1 class="fn org">Drooling Dog Bar B Q</h1><img class="rating average" src= "stars_map.png" alt="4
star rating" /><em>based on<span class="count">15</span> reviews</em></span>
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Get your markup with test tool
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How much structured data is out there?
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US/English Rich Snippets Usage
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Searches on Google with rich results
2x since Oct 2009
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Worldwide Rich Snippets Usage
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Searches on Google with rich results 4x since
Oct 2009
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RDFa on the rise (Peter Mika@W3C Bilbao)
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Future for Rich Snippets?
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Even Richer Snippets: Using information form the user's social graph, given granted access; Direct price comparison.
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Future for Rich Snippets?
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Even Richer Snippets using multimedia semantics.
Fake mock-up. Authors' private view.
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Schema.org
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Schema.rdfs.org
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2011/06/27: [Announcement]
We have posted an official version of the schema.org schemas at http://schema.org/docs/schemaorg.owl
“This allows the schema.org schemas to be used with all OWL-aware tools such as editors, validators etc., as well as to create mappings to other Semantic Web schemas. We would like to acknowledge the Linked Data Research Center at DERI, in particular Michael Hausenblas and Richard Cyganiak, for their work on schemas.rdfs.org, and for their help in developing the OWL schema for schema.org.”
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A lot of Events Categories in Schema.org
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8686
messagetake away
Lecture at the 2nd Summer School on Social Media Retrieval (S3MR) - Antalya,
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don't buryyour data in some HTML page
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data…when you publish a page that contains
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explicitdo make the embedding
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Linked Data Principles
Tim Berners Lee [2006] (Design Issues)
1. Use URIs to identify things (anything, not just documents);
2. Use HTTP URIs – globally unique names, distributed ownership –so that people can look up those names;
3. Provide useful information in RDF –when someone looks up a URI;
4. Include RDF links to other URIs –to enable discovery of related information
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An Example: DBpedia
DBpedia is a community effort to:extract structured "infobox" information from Wikipedia interlink DBpedia with other datasets on the Web
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Scraping infobox data
http://dbpedia.org/resource/Bogotá
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Automatic Links Among Open Datasets
<http://dbpedia.org/resource/Bogotá>owl:sameAs <http://sws.geonames.org/3688689/>owl:sameAs
<http://rdf.freebase.com/ns/guid.9202a8c04000641f8000000000167bab>dbpedia:population "6776009"...
<http://sws.geonames.org/3688689/>owl:sameAs <http://dbpedia.org/resource/Bogotá>wgs84_pos:lat "4.6"wgs84_pos:long "-74.0833333"geo:population "7102602"...
Geonames
DBpedia
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sameAs.org
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Bogotá on Freebase
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Bogotá on Geonames
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How Much Linked Data is there ?
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Linked Data Cloud – August 2007
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Linked Data Cloud – March 2008
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Linked Data Cloud – September 2008
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Linked Data Cloud – March 2009
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Linked Data Cloud – September 2010
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The Web of Data
Expose open datasets in RDF
Set RDF links among the data items for different datasets
Over 26 billion triples, 500 millions links, 203 datasets (September 2010)
... still counting
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… but let’s STOP counting!
Linked Open Numbers (April 1st 2010)
Linked Open Colors (April 1st 2011)http://purl.org/colors/
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Linked data summary
URIs, possibly identifying media fragments
+ annotations (tags)
+ links among fragments & annotations
105
dbpedia:Zidane
foaf:depicts
nar:location
geonames:2950159nar:subject
nc:15054000
events:id
wp:2006_FIFA_World_Cup#Final
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Searching Entities in the Cloud
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Reconciling links in the cloud
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Searching Linked Data
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Sindice already crawling Schema.org
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Browsing Linked Data
Tabulator(CSAIL, MIT)
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Browsing Linked Data
Disco(Free Universityof Berlin)
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Browsing Linked Data
Marbles(Free Universityof Berlin)
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Browsing Linked Data
Zitgist(Zitgist LLC)
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Browsing Linked Data
OpenLink Data Explorer(OpenLink Software)
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VisiNav
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Sig.ma
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TimBL Vision back in 1994
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FOAF History (credits: @danbri)
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Web pagesdescribethe World
Each makes‘claims’
They candisagree
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FOAF is a project about sharing information in the Web.It's about ways of describing things using computers, sothat those descriptions can be linked together, mixed up
with other data, and searched.
Friend of a Friend
People, groups, accounts, photos, IM, life on the Web.Machine-readable pages, de-centralised, freely extensible.
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Henry says, “My name is ‘Henry Story”
Joe says, “I know Henry who knows Jane”
Joe knows someone called “Henry Story”
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FOAF (Friend-of-a-Friend)
FOAF is an ontology for describing people and the relationships that exist between them
Can be integrated with any other SW vocabularies
Some services with FOAF exports:
People can also create their own FOAF document and link to it from their homepage
FOAF documents usually contain personal info, links to friends, and other related resources
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The FOAF Specification
http://xmlns.com/foaf/spec/ (3rd Edition, Jan 2010)
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Integrating SN with FOAF for reuse
Common formats,unique URIs
* Source: Sheila Kinsella, Applications of Social Network Analysis 2007
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Going through the Walled Gardens
David Simonds: Everywhere and nowhere. 19 May 2008, The Economist.
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FOAF Naut
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FOAF Builder
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FOAF hits the news
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Relationship Vocabulary
http://purl.org/vocab/relationship (Apr 2010)
35 new properties to complement FOAF
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acquaintance of
ambivalent of
ancestor of
apprentice to
child of
close friend of
collaborates with
colleague of employed by
enemy of
grandchild of
has met influenced by knows by reputation
knows in passing lives with
lost contact with
mentor of neighbor of parent of
participant relationship sibling of spouse of works with would like to now
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Semantically-Interlinked Online Communities
A schema for representing users, forums, posts and threads, containers, and other items in online community sites, for reuse and interoperability: Aims to fully describe the structure of content in these sites Also to create new connections between forums and posts from
different types of discussion systems (blogs, forums, mailing lists, etc.) and content items / containers on Web 2.0 sites
And to browse connected posts and channels in interesting ways (e.g. distributed linked conversations, decentralised discussion channels and communities, etc.)
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The SIOC ontology
http://rdfs.org/sioc/spec/ (March 2010)
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Producing SIOC data
Over 20 applications for producing SIOC data:Many are free and open sourceBlogs and forums: WordPress, phpBB, Drupal,
b2evolution “Legacy” applications: mailing lists, IRCNew media: Twitter, Jaiku, Facebook, Flickr
APIs for those who may wish to make their own producers:PHP, Perl, Java, Ruby on Rails
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136
Portable Data with SIOC and FOAF
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Collect SIOC from various sources
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Consuming SIOC via Exhibit
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Dublin Core
http://purl.org/dc/elements/1.1/ (Jan 2008)
15 elements or attribute-value pairs (simple DC)Contributor, Coverage, Creator, Date, Description,
Format, Identifier, Language, Publisher, Relation, Rights, Source, Subject, Title, Type
55 elements or attribute-value pairs (qualified DC)http://purl.org/dc/terms/http://purl.org/dc/dcmitype/http://purl.org/dc/dcam/
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Dublin Core example
<dc:title>Washing & ironing clothes.</dc:title>
<dc:date>ca. 1942</dc:date><dc:description>Mexican workers washing and ironingclothes.</dc:description>
<dc:subject> Agricultural laborers--Mexican--Oregon;Agricultural laborers--Housing--Oregon; Laundry
</dc:subject><dc:type>Image</dc:type><dc:source>Silver gelatin prints</dc:source><dc:rights> Permission to use must be obtained fromOSU Archives.</dc:rights>
<dc:identifier>P20:1069</dc:identifier><dc:identifier>http://digitalcollections.library.oregonstate.edu/u?/bracero,37</dc:identifier>
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Good Relations
http://purl.org/goodrelations/ (Apr 2010)
gr:BusinessEntity for a company or business,
gr:LocationOfSalesOrServiceProvisioning for a store,
gr:ProductOrServicesSomeInstancesPlaceholder for products or services (if there are multiple items),
gr:ActualProductOrServiceInstance for a particular product or service (e.g. used items),
gr:ProductOrServiceModel for the datasheet describing the features of a product, and
gr:Offering for an offer to sell, repair, lease something, or to express interest in such an offer.
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Best Buy
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At last years's SemTechconference, Myers saidthat it had resulted in a 30% increase in search traffic.
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The Open Graph Protocol
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Open Graph: Getting Started
<html xmlns:og="http://opengraphprotocol.org/schema/" xmlns:fb="http://www.facebook.com/2008/fbml"><head><title>The Rock (1996)</title><meta property="og:title" content="The Rock"/><meta property="og:type" content="movie"/><meta property="og:url“content="http://www.imdb.com/title/tt0117500/"/>
<meta property="og:image" content="http://ia.media-imdb.com/rock.jpg"/>
...</head> ... </html>
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Open Graph Properties
The Open Graph protocol defines 5 required properties: og:title - The title of your object as it should appear within the graph,
e.g., "The Rock". og:type - The type of your object, e.g., "movie". See also
http://developers.facebook.com/docs/opengraph#types og:image - An image URL which should represent your object within
the graph. The image must be at least 50px by 50px and have a maximum aspect ratio of 3:1.
og:url - The canonical URL of your object that will be used as its permanent ID in the graph, e.g., http://www.imdb.com/title/tt0117500/
og:site_name - A human-readable name for your site, e.g., "IMDb“
Optional properties og:description - A one to two sentence description of your page.* + location (7 properties) + contact (3 properties)
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rNews for the Press
RDFa vocabulary for news articles Easier to implement than
NewsML Easier to consume for news
search and other readers, aggregators
Under development at the IPTC March: v0.1 approved Final version by Sept
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Wrap up: popular vocabularies
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Agenda
A crash course in the world of structured data#microdata , #microformat , #rdfa#rdf , #owl , #skos , #sparql , #linkeddata
EventMedia (User-centered design approach)LODE: a model for representing eventsScraping and interlinking description of eventsEnriching events with illustrating mediaDetecting events from social media activities
Detecting events from human sensing#twitter , #foursquare , #facebook
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What are Events?
Events are observable occurrences grouping
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People Places Time
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Ontology: Making an abstraction
What? Where? When? Who?http://www.flickr.com/photos/benheine/473294112929/06/2011 - - 151Lecture at the 2nd Summer School on Social Media Retrieval (S3MR) - Antalya, Turkey
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Event-based centric interfaces
Action or occurrence taking place at a certain time at a specific locationUseful for organizing and browsing collections of mediaUseful for discovering complex relationships between
data
Need for an expressive event model for connecting pieces of data
Not Yet Another Model!
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There are already many event ontologies
Event Model Ontology URL
CIDOC CRM http://cidoc.ics.forth.gr/OWL/cidoc_v4.2.owl
ABC Ontology http://metadata.net/harmony/ABC/ABC.owl
Event Ontology http://purl.org/NET/c4dm/event.owl#
EventsML-G2 http://www.iptc.org/EventsML/
Dolce+DnS Ultralite http://www.loa-cnr.it/ontologies/DUL.owl
F http://events.semantic-multimedia.org/ontology/2008/12/15/model.owl
OpenCyc Ontology http://www.opencyc.org/
SEM http://semanticweb.cs.vu.nl/2009/04/event/
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Fundamental Types of Events
Aspect: ongoing activity vs transition between states cyc:Event ∩ cyc:StaticSituation ≤ cyc:Situation cidoc:E5.Event ∩ cidoc:E3.Condition_State ≤ cidco:E2.Temporal_Entity abc:Event is a transition between abc:Situation ≈ cidoc:E3.Condition_State
Agentivity: who has produced the event? cyc:Action, dul:Action ≤ Event E7.Activity ≤ E5.Event abc:Action ∩ abc:Event = Ø
Events are fully described as a set of actions taken by specific agentsIssue for modeling e.g. earthquakes
Interpretation matters! Identifiable changes or not? Agency can be assigned? dul:Situation describe dul:Event dul:Action, dul:Process ≤ dul:Event
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Events and Temporal Intervals
Relating events to chronological spans of time Persistent, socially attributed meanings Arbitrary system for subdividing an abstract space
Modeling a class for temporal intervals and use an OP ABC, CIDOC, EO (owl:TemporalEntity)
Modeling a XML Schema typed value and use a DP Pro: simplicity, values expressed as xsd:date or xsd:dateTime Cons: inability to express uncertain period or when there is no
coincidence with date units
Having two properties dul:hasEventDate ... litteral value dul:isObservableAt ... dul:TimeInterval
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Events, Spaces and Places
Relating events to places Semantically significant places Abstract spatial regions
Support spatial regions only: ABC, CIDOC, EO eo:Event eo:place wgs84:SpatialThing
cidoc:E5.Event cidoc:P7.took_place_at cidoc:E53.Place
Support the place/space distinction dul:Event dul:hasLocation dul:Place
dul:Event dul:hasRegion dul:SpaceRegion Most flexible approach: allow to resolve to places with no
geographical coordinate systems (e.g. mythical events, SecondLife)
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Participation in events
Object involvement in events: Simple involvement in event:
abc:Event abc:involves owl:Thing (≤ abc:Actuality)cidoc:E5.Event cidoc:P12.occurred_in_the_presence_of cidoc:E77dul:Event dul:hasParticipant dul:Object
eo:Event eo:factor owl:Thing
Tangible thing which results from an event:abc:Event abc:hasResult owl:Thing
eo:Event eo:product owl:Thing
Agent participation in events: abc:hasParticipant ≤ abc:hasPresence cidoc:P11.had_participant ≤ cidoc:P14.carried_out_by dul:involvesAgent ≤ abc:hasParticipant
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Events, Influence, Purpose and Causality
Making broad assertions linking events to any thing cidoc:P12.occurred_in_the_presence_of, cidoc:P15.was_influenced_by eo:factor, abc:hasResult
F model uses the DnS pattern
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Events, Parts and Composition
Event A being part of event B ≠ A's timespan ϵ B's timespan cidoc:P86.falls_within for expressing containment among timespans cidoc:P9.consist_of ≈ eo:sub_event ≈ abc:isSubEventOf
Linking sub-events with parthood dul:hasPart
The 20th century contains the year 1923World War II included Pearl Harbour
Linking sub-events with composition dul:hasConstituent
The French revolution is composed of the Bastille catch
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Towards a Linked Data Event Model
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Some mappings in LODE
ABC CIDOC DUL EO LODE
atTime P4.has_time_span isObservableAt time atTime
P7.took_place_at place inSpace
inPlace hasLocation atPlace
involves P12.occurred_in_the_presence_of
hasParticipant factor involved
hasPresence P11.had_participant involvesAgent agent involvedAgent
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Representing Events with
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1. Discover PAST, PRESENT and FUTURE events2. Live, relive and predict experiences through shared media3. Identify meaningful and/or interesting relationships
between events/media/people
EventMedia Goals (User-Centered Design)
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Online Survey (n=28), 2 group discussions (n=35)
Existing Technologies• Opinions• Interests• Suggestions• Benefits/drawbacks
Past Experiences (Memorable Events)• Discovery• Decision making• Registering & sharing • Meaningful relationships
ScenariosRequirements
1st Design Concept
1st Collect some opinions…
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EventMedia Project: Questionnaire
1. Think about a memorable/recent event you have participated: Tell us what it was and what type of event was it
2. How do you usually find out or look for such events?3. What is important to support your decision about
going to an event?4. Once you attended to an event, how do you register
the moment and share your experience?5. What could be considered meaningful (surprising or
entertaining) relationships among events?
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Brainstorm online with users
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2nd Look into “real” behaviors…
Scenario based study (2 sessions, n=15)
Scenarios
Opinions
Reenact
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Behavioral Patterns
Discovery Invitations and recommendations Rely on traditional mediaSocial networks (facebook - students)Previously attended events or venues
Decision MakingWho’s Joining? Where, When, How Much? (constraints)What? (e.g. type, performer, topic) Subjective factors (fun, atmosphere)
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Behavioral Patterns
Registering and SharingCommunicating their experiencePictures and short videos (for sharing)Media directories and social networks
Meaningful RelationshipsSimilar categories, attributes and contentUser attendance (similar interests, behaviors)Repeated events (e.g. annual festivals)
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Behavioral Patterns
EVENTEVENT
EVENT
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Existing Services
Single source with overview (?)
Allows opportunistic/serendipitous discovery
Limited exploration/browsing features
Information overload (cluttered, difficult)
Information incompleteness (coverage, decision)
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Organize the mess
Scrape event directories Link the information Find media illustrating events Design the application Interface
EventMedia
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Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
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Róisín Murphy at Nouveau Casino
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350591
E0-001-005971169-9
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Representing Events with
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Linking the Data
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Reasoning & Annotation
Time, Location and Attendance
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Collaborative Filtering
Disambiguate and propagate information about attendance
Identify Interests and provide Recommendations
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Interlinking
Linking Agents with Freebase, Dbpedia, MusicBrainz
Linking Venues with Geonames, Dbpedia, Foursquare (via Uberblic)
Linking Events with Last.fm, Upcoming, Eventful
Linking Categories with Facebook, Eventful, Upcoming, Zevents, LinkedIn,Eventbrite,
TicketMaster
Linking Users with Social Graph API
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Interlinking
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Event Media
MusicBrainz
DBpedia
Geonames
Foursquare
Uberblic
Freebase
Agent Last.fm Eventful MusicBrainz DBpedia Freebase Uberblic New York Times
Location Last.fm Eventful Upcoming DBpedia Freebase Foursquare Geonames
Event Last.fm Eventful Upcoming DBpedia Freebase Uberblic
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SILK Framework
Based on the Silk-LSL link specification language
Transformation and algebraic functions: max, min, avg, etc.
Several metrics available: Syntax: equality, Jaro, Leveinstein, n-gramLexical: WordNetGeo: wgs84Temporal: date
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Configuration SILK - LSL
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Silk Framework
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Alignement for Agents
Alignement base on: foaf:Agent rdfs:label
Examples : Donavan Frankenreiter / Donovan Frankenreiter (Jaro 0.98) × Birds & Batteries / Birds and Batteries (Jaro 0.70)
Total : Eventful : 61 % Last.fm : 58 %
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Eventful(6543)
Last.fm(50151)
MusicBrainz(459023)
Dbpedia(107112)
Uberblic(236691)
NYTimes(4794)
Eventful - 2865 (44%) 3616 (55%) 1985 (30%) 1567 (24%) 7 (0.1%)
Last.fm 2865 (6%) - 26619 (53%) 9442 (19%) 12905 (26%) 14 (0.03%)
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Alignement for Locations
Examples : The Stone Bar (34.1019 ;-118.304)
The Stone (34.1017 ;-118.304)
× fall harvest wine dinner bavarian inn restaurant frankenmuth (43.32 ; -83.73)
× Frankenmuth Bavarian Inn Restaurant (43.32 ; -83.74)
Total : Eventful : 17 % Last.fm : 15 % Upcoming : 36 %
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Eventful(13516)
Last.fm(15857)
Upcoming(5173)
DBpedia(496728)
Foursquare(641770)
Geonames(1090357)
Eventful - 998 (7%) 366 (3%) 90 (0,7%) 1296 (10%) 320 (2%)
Last.fm 998 (6%) - 626 (4%) 141 (0.9%) 911 (6%) 345 (2%)
Upcoming 366 (7%) 626 (12%) - 74 (1,4%) 1300 (25%) 232 (4%)
Dist : 29 m – Score (sim): 0.98
Dist : 80 m Score : 0.92
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Alignement for Events Alignement based on title, location and time
Example : LastFm : « Camp Bestival » à « Lulworth Castle » le 18/07/2008 Eventful : « New Camp Bestival Dorset » à « Lulworth Castle » le 18/07/2008
Total : Eventful : 0,4 % Last.fm : 3;8 % Upcoming : 4,8 %
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Eventful
(37647)
Last.fm
(57258)
Upcoming
(13114)
DBpediaMusic Festival
(662)
UberblicPerformer(228238)
Eventful - 76 (0,2%) 34 (0,1%) 28 (0,1%) 15 (0,04%)
Last.fm 76 (0,1%) - 586 (1%) 389 (0,7%) 1148 (2%)
Upcoming 34 (0,3%) 586 (4%) - 31 (0,2%) 15 (0,1%)
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Research challenges
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http://oaei.ontologymatching.org/2011/
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What are Events?
Events are observable occurrences grouping
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Experiences documented by Media
People Places Time
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Róisín Murphy at Nouveau Casino
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Media explicitly associated with the event
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1.7 million images over 108.000 events
Machine tags“lastfm:events”
APIs
4790 photos, 263 videos over 110 events
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Representing Media with Media Ontology
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How much data is there?
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Event Agent Location Photos UserLast.fm 57,258 50,150 16,471 1,425,318 18,542Upcoming 13,114 0 7,330 347,959 4,518Eventful 37,647 6,543 14,576 0 0Total 108,019 56,693 38,377 1,773,277 23,060
1,248,021 geo-tagged photos by propagating information from events!
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How fast media are uploaded?
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Finding more media that illustrate an event
A. Compute the bounding box area of a venue
B. Retrieve all media geo-tagged in this area
C. Retrieve all media with a similar title
D. Prune the results with visual analysis
E. Extend the result set with all media from the same uploader
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A. Bounding box of Nouveau Casino?
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B. 74 photos taken in this area this day
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C. 85 additional photos with a similar title
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D. 6 photos after visual pruning
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How is the visual pruning performed?
Model dataset: photo id + photo geo
Testing dataset: similar title
Low-level features used:Color moments, Gabor texture, Edge histogram
L1 distance on the K-nearest neighbors
ThresholdMin L1 distance between two model image pairsConservative approach
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E. 66 photos after uploader heuristics
cartoixa
hellerpop
13 photos
DustGraph / Stefan
46 photos
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Same process for videos
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1 video (id)3 videos (geo)26 videos (title)
Visual pruning performed on key framesNb positive > 50%
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How illustrated are events?
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Query By ID Query By Geo Query By Title Visual Pruning Heuristic
Photos 5 74 (74) 85 (85) 6 (6) 66 (66)
Videos(title)
1 3 (0) 23 (0) 13 (0) -
20 eventsModel dataset: 785 photosTesting dataset: 1766 photos (1573 positive, 193 negative)Results: 439 photos (99% precision, 28% recall)
Videos(title+venue)
10 (10)
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Generating Visual Summaries
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Generating Visual Summaries
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Generating Visual Summaries
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Generating Visual Summaries
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Generating Visual Summaries
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Event Detection
Detecting events by analyzing user activity on Flickr (uploading pattern)
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0
200
400
600
800
1000
1200
1400
Time
Accumulated Number of Uploading Photos
Possible Event
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Example: the venue Koko
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Ground truth obtained by scraping venue sitehttp://scraperwiki.com/profiles/Hou/
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Example: the venue Melkweg
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More events detected than event directories listings
Some events have no illustrative media
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Translating the Ontology and the Data
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Interface elements
Media
Facets Timeline
Location (Map)
Attendance
Events
Content and Background
Actions Sorting
Search
ME
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Interfaces
PerspectivesWhat: Event/Media CentricWho: Social Network VisualizationWhen: Time centricWhere: Location Centric
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http://www.flickr.com/photos/cocoarmani/1315402174
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The Back-end
RDF Repository on a web server with: Sesame2 SPARQL endpoint
with a distributed query engine.
A RESTful API that provides different methods and JSON representations of resources available in the dataset.
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RDFJSON
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User Interface
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User Interface
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Agenda
A crash course in the world of structured data#microdata , #microformat , #rdfa#rdf , #owl , #skos , #sparql , #linkeddata
EventMedia (User-centered design approach)LODE: a model for representing eventsScraping and interlinking description of eventsEnriching events with illustrating mediaDetecting events from social media activities
Detecting events from human sensing#twitter , #foursquare , #facebook
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Citizen Sensors in Action
Mumbai Terror Attack
Iran Election 2009
Haiti Earthquake 2010
US Healthcare Debate 2009
http://huff.to/hp0OhA
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Citizen Journalism
Twitter Journalism
Images: http://bit.ly/9GVfPQ, http://bit.ly/hmrTYV
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Business Intelligence
Trend Spotting, Forecasting, Brand Tracking, Targeted Advertising Sysomos: Business intelligence by engaging, measuring and understanding activities in Social Media Trendspotting: Detecting, analyzing and evaluating trends for business. Simplify: A collaborative platform to monitor, measure and engage customers using Social Media. Shoutlet: Managing social media marketing communication using a single platform.Reputation.com: Preserves privacy and defends reputation by protecting attacks on personal information.
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What’s in a Tweet?
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Metadata about People
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Identification
Network
Activity
Interests
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Metadata about People
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User Identification Metadata
• User-id• Screen/Display-name of user• Real name of user• Location • Profile Creation Date• User description
- Biodata of the user- Link to webpage of the user
Interest Metadata
• Author type - Trustee/donor, journalist, blogger, scientist etc.
• Favorite tweets• Types of lists subscribed • Style of Writing (personality
indicator)• No. of Followees• Majority of author type of Followees
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Metadata about People
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Activity Metadata
• Age of the profile
• Frequency of posts
• Timestamp of last status
• No. of Posts
• No. of Lists/groups created
• No. of Lists/groups subscribed
Influence Metadata (Inferring People Metadata from Network level Information)
• No. of Followers – normal, influential
• No. of Mentions
• No. of Retweets/Forwards
• No. of Replies
• No. of Lists/groups following
• No. of people following back
• Authority & Hub Scores
Web Presence:- User affiliations- KLOUT Score – influence measure (http://www.klout.com)
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Metadata about Network
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Structure Metadata
• Community Size• Community growth rate• Largest Strongly Connected Component size• Weakly Connected Components & Max(WCC) size• Average Degree of Separation• Clustering Coefficient
Relationship Metadata
• Type of Relationship• Relationship strength• User Homophily (based on certain characteristic such aslocation, interest etc.)• Reciprocity: mutual relationship• Active Community/ Ties
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Metadata about Content
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Extracting Entities from Tweets
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Facilitates understanding of multi-dimensional social perceptions over SMS, Tweets, multimedia Web content, electronic news media
Twitris: Semantic Social Web Mash-up
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Amit Sheth, http://twitris.knoesis.org/
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Searching on Twitter
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Issues with Multiple Keywords Search
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Let’s try to search with One Keyword
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Page 60!!
tweet I was looking for
Next Saturday @thatsimpsonguy aka Guilty Simpson will be performing atArea51 in my hometwon Eindhoven. #realliveshit #iwillspinrecordsabout 9 hours ago via Blackberry
Music Artist
Locations
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Relation Discovery Framework
news articles
microblogposts Entity
extraction &semantic
enrichment
Person A Location A
Location B
Event A
Group A
temporal constraints
relation type
weighting scheme
sourceselection
Relation discovery
Person A Location A
Group A
isLocatedIn
Person AinvolvedIn
typed relations
Applications- Browsing support- Query suggestions- Schema enrichment
Relation Discovery Framework
Ilknur Celik, Fabian Abel, Geert-Jan HoubenWeb Information Systems, TU Delft
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Entity Extraction and Semantic Enrichment
@bob: Julian Assange got arrested
Julian Assange
Tweet-basedenrichment
Julian AssangearrestedJulian Assange, the founder ofWikiLeaks, is under arrest inLondon…
News-basedenrichment
Julian Assange
London
WikiLeaks
powered by
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Relation Learning Strategies
Relation:
relation(e1, e2, type, tstart, tend, weight)
Relation Learning strategy: Input: entity e1 and e2, time period (tstart, tend) Challenge: infer weight and type of the relation for the given
Weighting according to co-occurrence frequency: Tweet-based: count co-occurrence in tweets News-based: count co-occurrence in news Tweet-News-based: count co-occurrence in both tweets and news
entities
type/label of relation
time period
relatedness
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time difference (in days) of first occurrence of relationship
News is faster Twitter is faster
Speed of strategies is domain-dependent
Where do relationships emerge faster?
http://wis.ewi.tudelft.nl/icwe2011/relation-learning/
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On Conferences … we Tweet
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Rich Activity Twitter Event Data
Take Twitter archives from TwapperKeeper
Enrich Tweets with relevant DBPediaconcepts using Zemanta
Rely on existing Linked Data about talks to perform the mappings.
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Milan Stankovic & Mattew Rowe: Mapping Tweets to Conference Talks, SDOW 2010
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Find the correspondence
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?
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meets
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Final Announcement: Google+
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Gundotra: “We believe online sharing is broken. And even awkward. We think connecting with other people is a basic human need. We do it all the time in real life, but our online tools are rigid. They force us into buckets — or into being completely public”
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Conclusions
The importance of structured data
EventMediaDataset part of the Semantic WebLODE used by the UK Archives HubMethod for finding media illustrating scheduled eventsMethod for detecting events from social mediaSocial Event Detection Task
Event-based approach for users to explore, annotate and share mediaUX can help semantics, semantics can help UXOutstanding challenges in interlinking and curating the data
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Credits
EURECOM: Houda Khrouf, Giuseppe Rizzo
CWI: Ryan Shaw, André Fialho, Lynda Hardman
Google/Yahoo!: Thomas Steiner, Peter Mika
Colleagues: Fabien Gandon, Alexandre Passant, Amit Sheth, Fabian Abel, Milan Stankovic, Matthew Rowe
… and the “media sharers”
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http://www.slideshare.net/troncy