The document discusses the use of citizen sensors for event monitoring and situational awareness, emphasizing the innovative dissemination of information during crises, like the 2008 Mumbai attacks. It highlights research challenges in spatio-temporal and thematic analysis, understanding casual text, and the integration of community-driven knowledge for semantic analytics. The text illustrates how domain models and social context contribute to enhanced situational awareness and knowledge extraction across various data sources.

1. mumbai, india

2. november 26, 2008

3. another chapter in the war against civilization

4. and

7. the world saw itThrough the eyes of the people

8. the world read itThrough the words of the people

9. PEOPLE told their stories to PEOPLE

10. A powerful new era in Information dissemination had taken firm ground

11. Making it possible for us tocreate a global network of citizensCitizen Sensors – Citizens observing, processing, transmitting, reporting

12. 12

13. Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awarenessAmit P. Sheth, LexisNexis Eminent ScholarKno.e.sis CenterWright State University

14. Geocoder(Reverse Geo-coding)Address to location database18 Hormusji Street, ColabaVasantViharImage Metadatalatitude: 18° 54′ 59.46″ N, longitude: 72° 49′ 39.65″ EStructured Meta ExtractionNariman HouseIncome Tax OfficeIdentify and extract information from tweetsSpatio-Temporal Analysis

15. Research Challenge #1Spatio Temporal and Thematic analysisWhat else happened “near” this event location?What events occurred “before” and “after” this event?Any message about “causes” for this event?

16. Spatial Analysis….Which tweets originated from an address near 18.916517°N 72.827682°E?

17. Which tweets originated during Nov 27th 2008,from 11PM to 12 PM

18. Giving usTweets originated from an address near 18.916517°N, 72.827682°E during time interval27th Nov 2008 between 11PM to 12PM?

19. Research Challenge #2:Understanding and Analyzing Casual TextCasual textMicroblogs are often written in SMS style languageSlangs, abbreviations

20. Understanding Casual TextNot the same as news articles or scientific literatureGrammatical errorsImplications on NL parser resultsInconsistent writing styleImplications on learning algorithms that generalize from corpus

21. Nature of MicroblogsAdditional constraint of limited contextMax. of x chars in a microblogContext often provided by the discourseEntity identification and disambiguationPre-requisite to other sophisticated information analytics

22. NL understanding is hard to begin with..Not so hard“commando raid appears to be nigh at Oberoinow”Oberoi = Oberoi Hotel, Nigh = highChallengingnew wing, live fire @ taj 2nd floor on iDesi TV streamFire on the second floor of the Taj hotel, not on iDesi TV

23. Social Context surrounding contentSocial context in which a message appears is also an added valuable resourcePost 1: “Hareemane Househostages said by eyewitnesses to be Jews. 7 Gunshots heard by reporters at Taj”Follow up postthat is Nariman House, not (Hareemane)

24. Research OpportunitiesNER, disambiguation in casual, informal text is a budding area of researchAnother important area of focus: Combining information of varied quality from a corpus (statistical NLP), domain knowledge (tags, folksonomies, taxonomies, ontologies), social context (explicit and implicit communities)

25. What Drives the Spatio-Temporal-Thematic Analysis and Casual Text UnderstandingSemantics with the help ofDomain ModelsDomain ModelsDomain Models(ontologies, folksonomies)

26. And who creates these models?YOU, ME,We DO!

27. Domain Knowledge: A key driverPlaces that are nearby ‘Nariman house’Spatial queryMessages originated around this placeTemporal analysisMessages about related events / placesThematic analysis

28. 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?

29. The Wisdom of the CrowdsThe most comprehensive and up to date account of the present state of knowledge is given byEverybodyThe Web in general

30. Blogs

31. WikipediaWikipedia = Concise concept descriptions + An article title denotes a concept + Community takes care of disambiguationCollecting Knowledge

32. 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 hierarchyCollecting Knowledge

33. Goal: Harness the Wisdom of the Crowds to Automatically define a domain with up-to-date conceptsWe can safely take advantage of existing (semi)structured knowledge sources

34. Collecting Instances

35. Creating a Hierarchy

36. Creating a Hierarchy

37. Hierarchy Creation - summary

38. Snapshot of final Topic Hierarchy

39. Great to know Explosion and Fire are related!But, knowing Explosion “causes” fire is powerfulRelationships at the heart of semantics!

40. Identifying relationships: Hard, harder than many hard things But NOT that Hard, When WE do it

41. Games with a purposeGet humans to give their solitaire time Solve real hard computational problemsImage tagging, Identifying part of an image Tag a tune, Squigl, Verbosity, and MatchinPioneered by Luis Von Ahn

42. OntoLablrRelationship Identification Gameleads to

43. causesExplosionTraffic congestion

44. And the infrastructureSemantic Sensor WebHow can we annotate and correlate the knowledge from machine sensors around the event location?

45. Research Challenge #4: Semantic Sensor Web

46. 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>

47. Semantic Sensor ML – Adding Ontological MetadataDomainOntologyPersonCompanySpatialOntologyCoordinatesCoordinate SystemTemporalOntologyTime UnitsTimezone45Mike Botts, "SensorML and Sensor Web Enablement," Earth System Science Center, UAB Huntsville

48. 46Semantic QuerySemantic Temporal QueryModel-references from SML to OWL-Time ontology concepts provides the ability to perform semantic temporal queriesSupported 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 intervalExample SPARQL query defining the temporal operator ‘within’

49. Kno.e.sis’ Semantic Sensor Web47

50. Synthetic but realistic scenarioan image taken from a raw satellite feed48

51. an image taken by a camera phone with an associated label, “explosion.” Synthetic but realistic scenario49

52. Textual messages (such as tweets) using STT analysisSynthetic but realistic scenario50

53. Correlating to getSynthetic but realistic scenario

54. Create better views (smart mashups)

55. A few more thingsUse of background knowledgeEvent extraction from texttime and location extraction Such information may not be presentSomeone from Washington DC can tweet about MumbaiScalable semantic analyticsSubgraph and pattern discoveryMeaningful subgraphs like relevant and interesting pathsRanking paths

56. The Sum of the PartsSpatio-Temporal analysisFind out where and when+ Thematic What and how+ Semantic Extraction from text, multimedia and sensor data - tags, time, location, concepts, events+ Semantic models & background knowledgeMaking better sense of STTIntegration + Semantic Sensor WebThe platform = Situational Awareness

57. SearchIntegrationAnalysisDiscoveryQuestion AnsweringSituational AwarenessDomain ModelsPatterns / Inference / ReasoningRDBRelationship WebMeta data / Semantic AnnotationsMetadata ExtractionMultimedia Content and Web dataTextSensor DataStructured and Semi-structured data

58. Interested in more background?Semantics-Empowered Social ComputingSemantic Sensor Web Traveling the Semantic Web through Space, Theme and Time Relationship Web: Blazing Semantic Trails between Web Resources Contact/more details: amit @ knoesis.orgSpecial thanks: Karthik Gomadam, MeenaNagarajan, Christopher ThomasPartial 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).