mumbai, india. november 26, 2008 another chapter in the war against civilization
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mumbai, india
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november 26, 2008
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another chapter in the war against civilization
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and
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the world saw it
Through the eyes of the people
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the world read itThrough the words of the people
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PEOPLE told their stories to PEOPLE
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A powerful new era in Information dissemination had
taken firm ground
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Making it possible for us to
create a global network of citizens
Citizen Sensors – Citizens observing, processing,
transmitting, reporting
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Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A
comprehensive path towards event monitoring and situational awareness
Amit P. Sheth, LexisNexis Eminent
ScholarKno.e.sis Center
Wright State University
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Image Metadatalatitude: 18° 54′ 59.46″ N, longitude: 72° 49′ 39.65″ E
Geocoder (Reverse Geo-coding)
Address to location database
18 Hormusji Street, Colaba
Nariman House
Identify and extract information from tweetsSpatio-Temporal Analysis
Structured Meta Extraction
Income Tax Office
Vasant Vihar
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Research Challenge #1
• Spatio Temporal and Thematic analysis– What else happened “near” this event
location?– What events occurred “before” and
“after” this event?– Any message about “causes” for this
event?
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Spatial Analysis….Which tweets originated from an
address near 18.916517°N 72.827682°E?
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Which tweets originated during Nov 27th 2008,from 11PM to 12 PM
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Giving us
Tweets originated from an address near 18.916517°N, 72.827682°E during time interval 27th Nov 2008 between 11PM to 12PM?
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Research Challenge #2:Understanding and Analyzing Casual Text
• Casual text– Microblogs are often written in SMS
style language– Slangs, abbreviations
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Understanding Casual Text
• Not the same as news articles or scientific literature– Grammatical errors
• Implications on NL parser results
– Inconsistent writing style• Implications on learning algorithms that
generalize from corpus
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Nature of Microblogs
• Additional constraint of limited context– Max. of x chars in a microblog– Context often provided by the discourse
• Entity identification and disambiguation
• Pre-requisite to other sophisticated information analytics
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NL understanding is hard to begin with..
• Not so hard– “commando raid appears to be nigh at
Oberoi now”• Oberoi = Oberoi Hotel, Nigh = high
• Challenging– new wing, live fire @ taj 2nd floor on
iDesi TV stream• Fire on the second floor of the Taj hotel, not
on iDesi TV
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Social Context surrounding content
• Social context in which a message appears is also an added valuable resource
• Post 1: – “Hareemane House hostages said by
eyewitnesses to be Jews. 7 Gunshots heard by reporters at Taj”
• Follow up post– that is Nariman House, not (Hareemane)
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What Drives the Spatio-Temporal-Thematic Analysis and Casual Text
Understanding
Semantics with the help of
1. Domain Models2. Domain Models3. Domain Models
(ontologies, folksonomies)
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And who creates these models?
YOU, ME,
We DO!
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Domain Knowledge: A key driver
• Places that are nearby ‘Nariman house’– Spatial query
• Messages originated around this place– Temporal analysis
• Messages about related events / places– Thematic analysis
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Research Challenge #3But Where does the Domain Knowledge come from?
• Community driven knowledge extraction – How to create models that are “socially
scalable”?– How to organically grow and maintain
this model?
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The Wisdom of the Crowds
• The most comprehensive and up to date account of the present state of knowledge is given by
Everybody= The Web in general= Blogs= Wikipedia
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• Wikipedia= Concise concept descriptions+ An article title denotes a concept
+ Community takes care of
disambiguation+ Large, highly connected, sparsely annotated graph structure that connects named entities+ Category hierarchy
Collecting Knowledge
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Goal: Harness the Wisdom of the Crowds to Automatically define a domain with up-to-date concepts
• We can safely take advantage of existing (semi)structured knowledge sources
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Collecting Instances
Seed Query
BWikipedia
Fulltext Concept Search (Somnath)
Graph Search
Graph Search(Denis)
Graph Search(Denis)
Graph Search(Denis)
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Hierarchy Creation - summary
Seed Query
BWikipedia
Fulltext Concept Search (Somnath)
Graph Search
Graph Search(Denis)
Graph Search(Denis)
Graph Search(Denis)
Hierarchy Creation
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Snapshot of final Topic Hierarchy
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Great to know Explosion and Fire are related!
But, knowing Explosion “causes” fire is powerful
Relationships at the heart of semantics!
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Identifying relationships: Hard, harder than many hard things
But NOT that Hard, When WE do it
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Games with a purpose
• Get humans to give their solitaire time – Solve real hard computational problems– Image tagging, Identifying part of an
image– Tag a tune, Squigl, Verbosity, and
Matchin– Pioneered by Luis Von Ahn
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OntoLablr
• Relationship Identification Game
•leads to•causes
Explosion Traffic congestion
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And the infrastructure
• Semantic Sensor Web– How can we annotate and correlate the
knowledge from machine sensors around the event location?
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Research Challenge #4: Semantic Sensor Web
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Semantically Annotated O&M
<swe:component name="time"><swe:Time definition="urn:ogc:def:phenomenon:time" uom="urn:ogc:def:unit:date-time">
<sa:swe rdfa:about="?time" rdfa:instanceof="time:Instant"><sa:sml rdfa:property="xs:date-time"/>
</sa:swe></swe:Time>
</swe:component><swe:component name="measured_air_temperature">
<swe:Quantity definition="urn:ogc:def:phenomenon:temperature“ uom="urn:ogc:def:unit:fahrenheit"><sa:swe rdfa:about="?measured_air_temperature“
rdfa:instanceof=“senso:TemperatureObservation"><sa:swe rdfa:property="weather:fahrenheit"/><sa:swe rdfa:rel="senso:occurred_when" resource="?time"/><sa:swe rdfa:rel="senso:observed_by" resource="senso:buckeye_sensor"/>
</sa:sml></swe:Quantity>
</swe:component>
<swe:value name=“weather-data">2008-03-08T05:00:00,29.1
</swe:value>
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Semantic Sensor ML – Adding Ontological Metadata
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Person
Company
Coordinates
Coordinate System
Time Units
Timezone
SpatialOntology
DomainOntology
TemporalOntology
Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville
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Semantic Query• Semantic Temporal Query
• Model-references from SML to OWL-Time ontology concepts provides the ability to perform semantic temporal queries
• Supported semantic query operators include:– contains: user-specified interval falls wholly within a sensor reading interval
(also called inside)– within: sensor reading interval falls wholly within the user-specified interval
(inverse of contains or inside)– overlaps: user-specified interval overlaps the sensor reading interval
• Example SPARQL query defining the temporal operator ‘within’
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Kno.e.sis’ Semantic Sensor Web
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Synthetic but realistic scenario
• an image taken from a raw satellite feed
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• an image taken by a camera phone with an associated label, “explosion.”
Synthetic but realistic scenario
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• Textual messages (such as tweets) using STT analysis
Synthetic but realistic scenario
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• Correlating to get
Synthetic but realistic scenario
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Create better views (smart mashups)
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A few more things
• Use of background knowledge• Event extraction from text
– time and location extraction • Such information may not be present• Someone from Washington DC can tweet about
Mumbai
• Scalable semantic analytics– Subgraph and pattern discovery
• Meaningful subgraphs like relevant and interesting paths
• Ranking paths
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The Sum of the Parts
Spatio-Temporal analysis– Find out where and when
+ Thematic – What and how
+ Semantic Extraction from text, multimedia and sensor data- tags, time, location, concepts, events
+ Semantic models & background knowledge– Making better sense of STT– Integration
+ Semantic Sensor Web– The platform
= Situational Awareness
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TextMultimedia Content
and Web data
Metadata Extraction
Patterns / Inference / Reasoning
Domain Models
Meta data / Semantic Annotations
Relationship Web
SearchIntegrationAnalysisDiscoveryQuestion AnsweringSituational Awareness
Sensor Data
RDB
Structured and Semi-structured data
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Interested in more background?
• Semantics-Empowered Social Computing
• Semantic Sensor Web • Traveling the Semantic Web through Spa
ce, Theme and Time • Relationship Web: Blazing Semantic Trai
ls between Web Resources
Contact/more details: amit @ knoesis.org
Special thanks: Karthik Gomadam, Meena Nagarajan, Christopher Thomas
Partial Funding: NSF (Semantic Discovery: IIS: 071441, Spatio Temporal Thematic: IIS-0842129), AFRL and DAGSI (Semantic Sensor Web), Microsoft Research and IBM Research (Analysis of Social Media Content),and HP Research (Knowledge Extraction from Community-Generated Content).
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