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Spatial Semantics for Better Interoperability and Analysis: Challenges And Experiences In Building Semantically Rich Applications In Web 3.0(Keynote at the 3rd Annual Spatial Ontology Community of Practice Workshop (SOCoP), USGS Reston, VA, December 03, 2010)Amit ShethLexisNexis Ohio Eminent ScholarOhio Center of Excellence in Knowledge-enabled Computing – Kno.e.sisWright State University, Dayton, OH http://knoesis.orgSemantic Provenance: Trusted Biomedical Data IntegrationThanks: Cory Henson, Prateek Jain& Kno.e.sis Team. Ack: NSF and otherFunding sources.
Semantics as core enabler, enhancer @ Kno.e.sis15 faculty45+ PhD students & post-docsExcellent Industry collaborations (MSFT, GOOG, IBM, Yahoo!, HP)Well fundedMultidisciplinaryExceptional Graduates2
Web (and associated computing) evolvingWeb ofpeople, Sensor Web   - social networks, user-created casual content   - 40 billion sensorsWeb of resources    - data, service, data, mashups    - 4 billion mobilecomputingWeb of databases   - dynamically generated pages   - web query interfacesWeb of pages   - text, manually created links   - extensive navigationComputing for Human ExperienceEnhanced Experience,Tech assimilated in lifehttp://bit.ly/HumanExperienceWeb as an oracle / assistant / partner   - “ask the Web”: using semantics to leveragetext + data + services2007Situations,EventsSemantic TechnologyUsedObjectsWeb 3.0Web 2.0Web 1.0PatternsKeywords1997
Variety & Growth of DataVariety/HeterogeneityMany intelligent applications that involve fusion and integrated analysis of wide variety of dataWeb pages/documents, databases, Sensor Data, Social/Community/Collective Data (Wikipedia), Real-time/Mobile/device/IoT data, Spatial Information, Background Knowledge (incl. Web of Data/Linked Open Data), Models/Ontologies…Exponential growth for each data: e.g. Mobile Data2009: 1 Exabyte (EB)2010 US alone: 40+ EB. Estimate of 2016-17 (Worldwide): 1 Zettabyte (ZB) or 1000 Exabytes. (Managing Growth & Profits in the Yottabytes Era, Chetan Sharma Consulting, 2009).
A large class of Web 3.0 applications…utilize larger amount of historical and recent/real-time data of various types from multiple sources (lot of data has spatial property)not only search, but analysis of or insight from data – that is applications are more “intelligent”This calls for semantics: spatial, temporal, thematic components;  background knowledgeThis talk: spatial semantics as a key component in building many Web 3.0 applications
A Challenging Example QueryWhat schools in Ohio should now be closed due to inclement weather?Need domain ontologies and rules to describe type of inclement weather and severity.Integrationof technologies needed to answer querySpatial AggregationSemantic  Sensor WebMachine PerceptionLinked Sensor DataAnalysis of Streaming Real-Time Data6
Technology 1Spatial Aggregation What schools are in Ohio?
 What weather sensors are near each of the school?7
Spatial AggregationUtilizes partonomy in order to aggregate spatial regionsTo query over spatial regions at different levels of granularityData represents “low-level” districts (school in district)
Query represents “high-level” state (school in state)8
Increased Availability of Spatial Info9
Accessing Can Be Difficult10
Must Ask for Information the “Right” Way11
Why is This Issue Relevant?Spatial data becoming more significant day by day.Crucial for multitude of applications:Social Networks like Twitter, Facebook…GPS Military Location Aware Services: Four Square Check-In weather data…Spatial Data availability on Web continuously increasing. Twitter Feeds, Facebook posts.Naïve users contribute and correct spatial data too which can lead to discrepancies in data representation.E.g. Geonames, Open Street Maps12
What We Want Automatically align conceptual mismatches User’s QuerySpatial Information of InterestSemantic Operators13
What is the Problem?Existing approaches only analyze spatial information and queries at the lexical and syntactic level.
Mismatches are common between how a query is expressed and how information of interest is represented.
Question: “Find schools in NJ”.
Answer: Sorry, no answers found!
Reason: Only counties are in states.
Natural language introduces much ambiguity for semantic relationships between entities in a query.
Find Schools in Greene County.14
What Needs to be Done?Reduce users’ burden of having to know how information of interest is represented and structured to enable access by broad population.Resolve mismatches between a query and information of interest due to differences in granularity to improve recall of relevant information. Resolve ambiguous relationships between entities based on natural language to reduce the amount of wrong information retrieved.15
Existing Mechanism for Querying RDFSPARQLRegular Expression Based Querying Approaches16
Common Query Testing All Approaches“Find Schools Located in the State of Ohio”17
In a Perfect Scenarioparent featureSchoolOhio18
In a Not so Perfect ScenarioCountyparent featureSchoolOhioparent feature19
Countyparent featureOhioparent featureSchoolAnd Finally..Exchange students parent featureSchoolIndiana20
Proposed ApproachDefine operators to ease writing of expressive queries by implicit usage of semantic relations between query terms and hence remove the burden of expressing named relations in a query. Define transformation rules for operators based on work by Winston’s taxonomy of part-whole relations.Rule based approach allows applicability in different domains with appropriate modifications.PartonomicalRelationship Based Query Rewriting System (PARQ) implements this approach.21
Meta Rules for Winston’s Categories Transitivity(a φ-part of b)	(b φ-part of c)	(a φ-part of c)Dayton place-part of OhioOhio place-part of USDayton place-part of USOverlap(a place-part of b)	(a place-part of b)	(b overlaps c)Sri Lank place-part of Indian OceanSri Lank place-part of Bay of BengalIndian Ocean overlaps with Bay of BengalSpatial Inclusion(a place-part of b)	(a place-part of b)	(b overlaps c)White House instance of BuildingBarack is in the White HouseBarack isIn the building22
Slight and Severe MismatchSELECT ?schoolWHERE 	{	?state	geo:featureClassgeo:A	?schools	geo:featureClassgeo:S	?state	geo:name	"Ohio“	     ?schools	geo:parentFeature	?state }Query Re-WriterSELECT ?schoolWHERE	{	?state        geo:featureClassgeo:A	?schools    geo:featureClassgeo:S	?state        	geo:name               	"Ohio“	?school    geo:parentFeature    	?county 	?county	geo:parentFeature    	?state	}23
Where Do We Stand With All Mechanisms..24
EvaluationPerformed on publicly available datasets (Geonames and British Ordnance Survey Ontology)Utilized 120 questions  from National Geographic Bee and 46 questions from trivia related to British Administrative GeographyQuestions serialized into SPARQL Queries by 4 human respondents unfamiliar with ontologyPerformance of PARQ compared with PSPARQL and SPARQL25
Sample Queries“In which English county, also known as "The Jurassic Coast" because of the many fossils to be found there, will you find the village of Beer Hackett?”“The Gobi Desert is the main physical feature in the southern half of a country also known as the homeland of Genghis Khan. Name this country.”26
PARQ  - vs -  SPARQL27
PARQ  - vs -  PSPARQLComparison for National  Geographic Bee over GeonamesComparison for British Admin. Trivia over Ordnance Survey Dataset28
Spatial Aggregation ConclusionQuery engines expect users to know the dataset structure and pose well formed queriesQuery engines ignore semantic relations between query termsNeed to exploit semantic relations between concepts  for processing queriesNeed to provide systems with behind the scenes rewrite of queries to remove burden of knowing structure of data29
Technology 2Semantic Sensor Web (SSW) What is inclement weather?
 What sensors in Ohio are capable of detecting inclement weather?
 What sensors are near schools in Ohio?
 What observations are these sensors generating NOW?
 Are these observations providing evidence for inclement weather?30
Semantic Sensor WebUtilizes ontologies to represent and analyze heterogeneous sensor dataSensor-observation ontology
Spatial ontology
Temporal ontology
Domain ontologies (i.e., weather ontology)Generates abstractions (that matter to human decision making) over sensor dataAnalysis of data to detect and represent interesting features (i.e., objects, events, situations)Utilizes semantic technologies to bridge the divide between the “real-world” and the Web (critical to Cyber-Physical systems)EnvironmentSensorObservationPhysical Space (“real-world”)Information Space (Web)PerceptionEvent ID/Understanding,Situation AwarenessSensor Data32Semantic Sensor Web
Sensors are now ubiquitous, 	and constantly generating observations about our world33
However, these systems are often stovepiped,	with strong tie between sensor network and application34
We want to set this data free35
With freedom comes new responsibilities ….36
	Web Services		- OGC Sensor Web Enablement (SWE)1) How to discover, access and search the data?37
when it comes from many different sources?	Shared knowledge models, or Ontologies		- syntactic models – XML (SWE)		- semantic models – OWL/RDF (W3C SSN-XG)2) How to integrate this data together38
The SSN-XG Deliverables Ontology for semantically describing sensors
Illustrate the relationship to OGC Sensor Web Enablement standards
Semantic annotation of OGC Sensor Web Enablement standards39
3) Make streaming numerical sensor data meaningful to web applications and naïve users?Symbols more meaningful than numbers		- analysis and reasoning (understanding through perception)
Overall Architecture
SSW demo with Mesowest datahttp://knoesis.org/projects/sensorweb/demos/semsos_mesowest/ssos_demo.htm
Technology 3Active Machine Perception Are these observations providing evidence for    inclement weather?43
Machine PerceptionTask of extracting meaning from sensor dataPerception is the act of choosing from alternative explanations for a set of observations (Intellego Perception)Perception is a active, cyclical process of explaining observations by actively seeking – or focusing on – additional information (Active Perception)Active Perception cycle is driven by prior knowledge44
Goal to ObtainAwareness of the SituationWebobserveperceive“Real-World”45
Formal Theory of Machine PerceptionSpecificationImplementationEvaluationOntology of Perception: A Semantic Web Approach to Enhance Machine Perception (Technical Report, Sept. 2010)46
Enable Situation Awareness on WebMust utilize abstractionscapable of representing observations and perceptions generated by either people or machines.Webobserveperceive“Real-World”47
Observation of QualitiesBoth people and machines are capable of observing qualities, such as redness.observesObserverQualityFormally described in a sensor/observation ontology48
Perception of EntitiesBoth people and machines are also capable of perceiving entities, such as applesperceivesPerceiverEntity* Formally described in a perception ontology49
Background KnowledgeAbility to perceive is afforded through the use of background knowledge. For example, knowledge that apples are red helps to infer an apple from an observed quality of redness. Qualityinheres inEntityFormally described in a domain ontology50
Perception CycleThe ability to perceive efficiently is afforded through the cyclical exchange of information between observers and perceivers. Observersendsfocussends perceptPerceiverTraditionally called the Perception Cycle (or Active Perception)51
Integrated Perception CycleIntegrated together, we have an abstract model – capable of situation awareness – relating observers, perceivers, and background knowledge.observesObserverQualitysends perceptsendsfocusinheres inperceivesPerceiverEntity52
Specification of Perception Cycle (in set theory)53
Implementation of Perception Cycle5454
Evaluation of Perception CycleWe demonstrated 50% savings in resource requirementsby utilizing background knowledge within the Perception Cycle5555
Trusted Perception Cycle Demohttp://www.youtube.com/watch?v=lTxzghCjGgUhttp://knoesis.org/projects/sensorweb/demos/trusted_perception_cycle/
Technology 4Linked Sensor Data What schools are in Ohio?
 What inclement weather necessitates school closings?
 What sensors in Ohio are capable of detecting inclement weather?
 What sensors are near schools in Ohio?
 What observations are these sensors generating NOW?57
Linked Sensor DataKnowledge/representations from SSW are accessible on LODLinkedSensorDataDescriptions of ~20,000 weather stations
Weather stations linked to featured defined in Geonames.org
LinkedObservationData
Description of storm related observations
~1.7 billion triples, ~170 million weather observations
Updated in real-time with current observations and abstractions58
Linked Open DataCommunity-led effort to create openly accessible, and interlinked, semantic (RDF) data on the Web59

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Spatial Semantics for Better Interoperability and Analysis: Challenges and Experiences in Building Semantically Rich Applications in Web 3.0

  • 1. Spatial Semantics for Better Interoperability and Analysis: Challenges And Experiences In Building Semantically Rich Applications In Web 3.0(Keynote at the 3rd Annual Spatial Ontology Community of Practice Workshop (SOCoP), USGS Reston, VA, December 03, 2010)Amit ShethLexisNexis Ohio Eminent ScholarOhio Center of Excellence in Knowledge-enabled Computing – Kno.e.sisWright State University, Dayton, OH http://knoesis.orgSemantic Provenance: Trusted Biomedical Data IntegrationThanks: Cory Henson, Prateek Jain& Kno.e.sis Team. Ack: NSF and otherFunding sources.
  • 2. Semantics as core enabler, enhancer @ Kno.e.sis15 faculty45+ PhD students & post-docsExcellent Industry collaborations (MSFT, GOOG, IBM, Yahoo!, HP)Well fundedMultidisciplinaryExceptional Graduates2
  • 3. Web (and associated computing) evolvingWeb ofpeople, Sensor Web - social networks, user-created casual content - 40 billion sensorsWeb of resources - data, service, data, mashups - 4 billion mobilecomputingWeb of databases - dynamically generated pages - web query interfacesWeb of pages - text, manually created links - extensive navigationComputing for Human ExperienceEnhanced Experience,Tech assimilated in lifehttp://bit.ly/HumanExperienceWeb as an oracle / assistant / partner - “ask the Web”: using semantics to leveragetext + data + services2007Situations,EventsSemantic TechnologyUsedObjectsWeb 3.0Web 2.0Web 1.0PatternsKeywords1997
  • 4. Variety & Growth of DataVariety/HeterogeneityMany intelligent applications that involve fusion and integrated analysis of wide variety of dataWeb pages/documents, databases, Sensor Data, Social/Community/Collective Data (Wikipedia), Real-time/Mobile/device/IoT data, Spatial Information, Background Knowledge (incl. Web of Data/Linked Open Data), Models/Ontologies…Exponential growth for each data: e.g. Mobile Data2009: 1 Exabyte (EB)2010 US alone: 40+ EB. Estimate of 2016-17 (Worldwide): 1 Zettabyte (ZB) or 1000 Exabytes. (Managing Growth & Profits in the Yottabytes Era, Chetan Sharma Consulting, 2009).
  • 5. A large class of Web 3.0 applications…utilize larger amount of historical and recent/real-time data of various types from multiple sources (lot of data has spatial property)not only search, but analysis of or insight from data – that is applications are more “intelligent”This calls for semantics: spatial, temporal, thematic components; background knowledgeThis talk: spatial semantics as a key component in building many Web 3.0 applications
  • 6. A Challenging Example QueryWhat schools in Ohio should now be closed due to inclement weather?Need domain ontologies and rules to describe type of inclement weather and severity.Integrationof technologies needed to answer querySpatial AggregationSemantic Sensor WebMachine PerceptionLinked Sensor DataAnalysis of Streaming Real-Time Data6
  • 7. Technology 1Spatial Aggregation What schools are in Ohio?
  • 8. What weather sensors are near each of the school?7
  • 9. Spatial AggregationUtilizes partonomy in order to aggregate spatial regionsTo query over spatial regions at different levels of granularityData represents “low-level” districts (school in district)
  • 10. Query represents “high-level” state (school in state)8
  • 12. Accessing Can Be Difficult10
  • 13. Must Ask for Information the “Right” Way11
  • 14. Why is This Issue Relevant?Spatial data becoming more significant day by day.Crucial for multitude of applications:Social Networks like Twitter, Facebook…GPS Military Location Aware Services: Four Square Check-In weather data…Spatial Data availability on Web continuously increasing. Twitter Feeds, Facebook posts.Naïve users contribute and correct spatial data too which can lead to discrepancies in data representation.E.g. Geonames, Open Street Maps12
  • 15. What We Want Automatically align conceptual mismatches User’s QuerySpatial Information of InterestSemantic Operators13
  • 16. What is the Problem?Existing approaches only analyze spatial information and queries at the lexical and syntactic level.
  • 17. Mismatches are common between how a query is expressed and how information of interest is represented.
  • 19. Answer: Sorry, no answers found!
  • 20. Reason: Only counties are in states.
  • 21. Natural language introduces much ambiguity for semantic relationships between entities in a query.
  • 22. Find Schools in Greene County.14
  • 23. What Needs to be Done?Reduce users’ burden of having to know how information of interest is represented and structured to enable access by broad population.Resolve mismatches between a query and information of interest due to differences in granularity to improve recall of relevant information. Resolve ambiguous relationships between entities based on natural language to reduce the amount of wrong information retrieved.15
  • 24. Existing Mechanism for Querying RDFSPARQLRegular Expression Based Querying Approaches16
  • 25. Common Query Testing All Approaches“Find Schools Located in the State of Ohio”17
  • 26. In a Perfect Scenarioparent featureSchoolOhio18
  • 27. In a Not so Perfect ScenarioCountyparent featureSchoolOhioparent feature19
  • 28. Countyparent featureOhioparent featureSchoolAnd Finally..Exchange students parent featureSchoolIndiana20
  • 29. Proposed ApproachDefine operators to ease writing of expressive queries by implicit usage of semantic relations between query terms and hence remove the burden of expressing named relations in a query. Define transformation rules for operators based on work by Winston’s taxonomy of part-whole relations.Rule based approach allows applicability in different domains with appropriate modifications.PartonomicalRelationship Based Query Rewriting System (PARQ) implements this approach.21
  • 30. Meta Rules for Winston’s Categories Transitivity(a φ-part of b) (b φ-part of c) (a φ-part of c)Dayton place-part of OhioOhio place-part of USDayton place-part of USOverlap(a place-part of b) (a place-part of b) (b overlaps c)Sri Lank place-part of Indian OceanSri Lank place-part of Bay of BengalIndian Ocean overlaps with Bay of BengalSpatial Inclusion(a place-part of b) (a place-part of b) (b overlaps c)White House instance of BuildingBarack is in the White HouseBarack isIn the building22
  • 31. Slight and Severe MismatchSELECT ?schoolWHERE { ?state geo:featureClassgeo:A ?schools geo:featureClassgeo:S ?state geo:name "Ohio“ ?schools geo:parentFeature ?state }Query Re-WriterSELECT ?schoolWHERE { ?state geo:featureClassgeo:A ?schools geo:featureClassgeo:S ?state geo:name "Ohio“ ?school geo:parentFeature ?county ?county geo:parentFeature ?state }23
  • 32. Where Do We Stand With All Mechanisms..24
  • 33. EvaluationPerformed on publicly available datasets (Geonames and British Ordnance Survey Ontology)Utilized 120 questions from National Geographic Bee and 46 questions from trivia related to British Administrative GeographyQuestions serialized into SPARQL Queries by 4 human respondents unfamiliar with ontologyPerformance of PARQ compared with PSPARQL and SPARQL25
  • 34. Sample Queries“In which English county, also known as "The Jurassic Coast" because of the many fossils to be found there, will you find the village of Beer Hackett?”“The Gobi Desert is the main physical feature in the southern half of a country also known as the homeland of Genghis Khan. Name this country.”26
  • 35. PARQ - vs - SPARQL27
  • 36. PARQ - vs - PSPARQLComparison for National Geographic Bee over GeonamesComparison for British Admin. Trivia over Ordnance Survey Dataset28
  • 37. Spatial Aggregation ConclusionQuery engines expect users to know the dataset structure and pose well formed queriesQuery engines ignore semantic relations between query termsNeed to exploit semantic relations between concepts for processing queriesNeed to provide systems with behind the scenes rewrite of queries to remove burden of knowing structure of data29
  • 38. Technology 2Semantic Sensor Web (SSW) What is inclement weather?
  • 39. What sensors in Ohio are capable of detecting inclement weather?
  • 40. What sensors are near schools in Ohio?
  • 41. What observations are these sensors generating NOW?
  • 42. Are these observations providing evidence for inclement weather?30
  • 43. Semantic Sensor WebUtilizes ontologies to represent and analyze heterogeneous sensor dataSensor-observation ontology
  • 46. Domain ontologies (i.e., weather ontology)Generates abstractions (that matter to human decision making) over sensor dataAnalysis of data to detect and represent interesting features (i.e., objects, events, situations)Utilizes semantic technologies to bridge the divide between the “real-world” and the Web (critical to Cyber-Physical systems)EnvironmentSensorObservationPhysical Space (“real-world”)Information Space (Web)PerceptionEvent ID/Understanding,Situation AwarenessSensor Data32Semantic Sensor Web
  • 47. Sensors are now ubiquitous, and constantly generating observations about our world33
  • 48. However, these systems are often stovepiped, with strong tie between sensor network and application34
  • 49. We want to set this data free35
  • 50. With freedom comes new responsibilities ….36
  • 51. Web Services - OGC Sensor Web Enablement (SWE)1) How to discover, access and search the data?37
  • 52. when it comes from many different sources? Shared knowledge models, or Ontologies - syntactic models – XML (SWE) - semantic models – OWL/RDF (W3C SSN-XG)2) How to integrate this data together38
  • 53. The SSN-XG Deliverables Ontology for semantically describing sensors
  • 54. Illustrate the relationship to OGC Sensor Web Enablement standards
  • 55. Semantic annotation of OGC Sensor Web Enablement standards39
  • 56. 3) Make streaming numerical sensor data meaningful to web applications and naïve users?Symbols more meaningful than numbers - analysis and reasoning (understanding through perception)
  • 58. SSW demo with Mesowest datahttp://knoesis.org/projects/sensorweb/demos/semsos_mesowest/ssos_demo.htm
  • 59. Technology 3Active Machine Perception Are these observations providing evidence for inclement weather?43
  • 60. Machine PerceptionTask of extracting meaning from sensor dataPerception is the act of choosing from alternative explanations for a set of observations (Intellego Perception)Perception is a active, cyclical process of explaining observations by actively seeking – or focusing on – additional information (Active Perception)Active Perception cycle is driven by prior knowledge44
  • 61. Goal to ObtainAwareness of the SituationWebobserveperceive“Real-World”45
  • 62. Formal Theory of Machine PerceptionSpecificationImplementationEvaluationOntology of Perception: A Semantic Web Approach to Enhance Machine Perception (Technical Report, Sept. 2010)46
  • 63. Enable Situation Awareness on WebMust utilize abstractionscapable of representing observations and perceptions generated by either people or machines.Webobserveperceive“Real-World”47
  • 64. Observation of QualitiesBoth people and machines are capable of observing qualities, such as redness.observesObserverQualityFormally described in a sensor/observation ontology48
  • 65. Perception of EntitiesBoth people and machines are also capable of perceiving entities, such as applesperceivesPerceiverEntity* Formally described in a perception ontology49
  • 66. Background KnowledgeAbility to perceive is afforded through the use of background knowledge. For example, knowledge that apples are red helps to infer an apple from an observed quality of redness. Qualityinheres inEntityFormally described in a domain ontology50
  • 67. Perception CycleThe ability to perceive efficiently is afforded through the cyclical exchange of information between observers and perceivers. Observersendsfocussends perceptPerceiverTraditionally called the Perception Cycle (or Active Perception)51
  • 68. Integrated Perception CycleIntegrated together, we have an abstract model – capable of situation awareness – relating observers, perceivers, and background knowledge.observesObserverQualitysends perceptsendsfocusinheres inperceivesPerceiverEntity52
  • 69. Specification of Perception Cycle (in set theory)53
  • 71. Evaluation of Perception CycleWe demonstrated 50% savings in resource requirementsby utilizing background knowledge within the Perception Cycle5555
  • 72. Trusted Perception Cycle Demohttp://www.youtube.com/watch?v=lTxzghCjGgUhttp://knoesis.org/projects/sensorweb/demos/trusted_perception_cycle/
  • 73. Technology 4Linked Sensor Data What schools are in Ohio?
  • 74. What inclement weather necessitates school closings?
  • 75. What sensors in Ohio are capable of detecting inclement weather?
  • 76. What sensors are near schools in Ohio?
  • 77. What observations are these sensors generating NOW?57
  • 78. Linked Sensor DataKnowledge/representations from SSW are accessible on LODLinkedSensorDataDescriptions of ~20,000 weather stations
  • 79. Weather stations linked to featured defined in Geonames.org
  • 81. Description of storm related observations
  • 82. ~1.7 billion triples, ~170 million weather observations
  • 83. Updated in real-time with current observations and abstractions58
  • 84. Linked Open DataCommunity-led effort to create openly accessible, and interlinked, semantic (RDF) data on the Web59
  • 85. What is Linked Sensor DataWeather SensorsSensor DatasetGPS SensorsSatellite SensorsCamera Sensors60
  • 86. RDF descriptions of ~20,000 weather stations in the United States.
  • 87. Observation dataset linked to sensors descriptions.
  • 88. Sensors link to locations in Geonames (in LOD) that are nearby.weather stationSensors Dataset (LinkedSensorData)**First Initiative for exposing Sensor Data on LOD61
  • 89. What is Linked Sensor DataRecommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Web using URIs and RDFGeoNames DatasetRDF – language for representing data on the WeblocatedNearSensor DatasetPublicly Accessible62
  • 90. Observations Dataset (LinkedObservationData) – Static DatasetsRDF descriptions of hurricane and blizzard observations in the United States.
  • 91. The data originated at MesoWest (University of Utah)
  • 92. Observation types: temperature, visibility, precipitation, pressure, wind speed, humidity, etc. 63
  • 102. Sensor Discovery Application Weather Station IDCurrent Observations from MesoWestWeather Station CoordinatesWeather Station PhenomenaMesoWest – Project under Department of Meteorology, University of UTAHGeoNames – Geographic dataset65
  • 103. Sensor Discovery on Linked Data Demohttp://knoesis.org/projects/sensorweb/demos/sensor_discovery_on_lod/sample.htm
  • 104. Technology 5Analysis of Streaming Real-Time Data What observations are these sensors generating NOW?67
  • 105. Analysis of Streaming Real-Time DataConversion from raw data to semantically annotated data in real-timeAnalysis of data to generate abstractions in real-time
  • 106. Real Time Streaming Sensor DataSemantic Analysis using Ontology for Event DetectionStoring Abstractions (Events) obtained after reasoning on the LOD
  • 109. Too Much Data(Data grows faster than storage!!)72
  • 110. SolutionHuge amounts of Sensor Data!!Abstractions over data (Events)Observations relevant to events73
  • 111. Workflow Architecture for Managing Streaming Sensor Data
  • 113. The Query What schools in Ohio should now be closed due to inclement weather? needs to be divided into sub-queries that can be answered using technologies previously described76
  • 114. What Schools Are in Ohio?Need partonomical spatial relationsWhat counties are contained in Ohio?
  • 115. What districts are contained in a county?
  • 116. What schools are contained in a district?
  • 117. Geonames.org contains these partonomical spatial relations
  • 118. Spatial aggregation executes the partonomical inference to convert the general query into sub-queries that can be answeredUses: spatial aggregation and LOD77
  • 119. What is Inclement Weather?Need domain ontology that describes characteristics of inclemental weatherExampleIcy Roads => freezing temperature & precipitation (rain or snow)Uses: SSW78
  • 120. What Inclement Weather Necessitates School Closings?Need school policy information on rules for closing (e.g., for icy road conditions)Data.gov on LOD contains large amount of such policy informationUses: LOD79
  • 121. What Sensors in Ohio Are Capable of Detecting Inclement Weather?Need ontological descriptions of sensors and weather in order to match sensor capabilities to weather characteristicsTemperature sensor  freezing temperature
  • 122. Rain gauge sensor  precipitationLinkedSensorData has descriptions of ~20,000 weather stations on LODUses: SSW and LOD80
  • 123. Sensors Near Schools in Ohio?Spatial analysis: match school locations (in Ohio) to sensor locations that are nearbySensor descriptions in LinkedSensorData contain links to nearby features (such as schools)Uses: SSW and LOD81
  • 124. What Observations are These Sensors Generating NOW?Need to semantically annotate raw streaming observations in real-timeNeed to make these current/real-time annotations accessible by placing them on LOD (i.e., LinkedObservationData)Uses: SSW, LOD, Streaming Data82
  • 125. Are These Observations Providing Evidence for Inclement Weather?Analysis of observation data using background knowledgeGeneration of abstractions that are easier to understandUses: SSW, Perception83
  • 126. ReferencesSpatial Aggregation References (http://knoesis.org/research/semweb/projects/stt/)Prateek Jain, Peter Z. Yeh, KunalVerma, Cory Henson and AmitSheth, SPARQL Query Re-writing for Spatial Datasets Using Partonomy Based Transformation Rules, 3rd Intl. Conference on Geospatial Semantics (GeoS 2009), Mexico City, Mexico, December 3-4, 2009.Alkhateeb, F., Baget, J.-F., Euzenat, J.: Extending SPARQL with regular expression patterns (for querying RDF). Web Semantics 7, 2009.Semantic Sensor Web References (http://wiki.knoesis.org/index.php/SSW)Cory Henson, Josh Pschorr,AmitSheth, KrishnaprasadThirunarayan, SemSOS: Semantic Sensor Observation Service, in Proceedings of the 2009 International Symposium on Collaborative Technologies and Systems (CTS 2009), Baltimore, MD, May 18-22, 2009.Cory Henson, HolgerNeuhaus, AmitSheth, KrishnaprasadThirunarayan, RajkumarBuyya, An Ontological Representation of Time Series Observations on the Semantic Sensor Web, in Proceedings of 1st International Workshop on the Semantic Sensor Web 2009.Michael Compton, Cory Henson, Laurent Lefort, HolgerNeuhaus, A Survey of the Semantic Specification of Sensors, 2nd International Workshop on Semantic Sensor Networks, 25-29 October 2009, Washington DC.Machine Active Perception ReferencesCory Henson, KrishnaprasadThirunarayan, PramodAnatharam, AmitSheth, Making Sense of Sensor Data through a Semantics Driven Perception Cycle, Kno.e.sis Center Technical Report, 2010.KrishnaprasadThirunarayan, Cory Henson, AmitSheth, Situation Awareness via Abductive Reasoning for Semantic Sensor Data: A Preliminary Report, In: Proceedings of 2009 International Symposium on Collaborative Technologies and Systems (CTS 2009), pp. 111-118, May 18-22, 2009.Ontology of Perception (for distribution limited to SOCoP workshop participants only).Linked Sensor Data References (http://wiki.knoesis.org/index.php/LinkedSensorData)HarshalPatni, Cory Henson, AmitSheth, Linked Sensor Data, In: Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010.HarshalPatni, Satya S. Sahoo, Cory Henson and AmitSheth, Provenance Aware Linked Sensor Data, 2nd Workshop on Trust and Privacy on the Social and Semantic Web, Co-located with ESWC, Heraklion Greece, 30th May - 03 June 2010Joshua Pschorr, Cory Henson, HarshalPatni, Amit P. Sheth, Sensor Discovery on Linked Data, Kno.e.sis Center Technical Report, 2010

Editor's Notes

  • #2: Knoesis center recently declared a center of excellence by Ohio governor: http://bit.ly/coe-k
  • #8: Knoesis center recently declared a center of excellence by Ohio governor
  • #14: A mechanism to improve access to spatial information by allowing users
  • #31: Knoesis center recently declared a center of excellence by Ohio governor
  • #37: Making sense of data from many sources in many form is challenging
  • #44: Knoesis center recently declared a center of excellence by Ohio governor
  • #49: Observer - agent that executes an observation process.Observation Process – process of detecting qualities from stimuli, consisting of the following steps: (1) choose an observable quality, (2) find stimuli that are causally linked to the observable quality, (3) detect the stimuli, and (4) generate observation values. Observation values (or percepts) are symbols that represent the observed qualities in the physical world.Observation – action by an observer of executing the observation process
  • #50: Perceiver - agent that executes a perception processPerception Process – process of detecting entities from observed qualities (abductive inference). Note that the perception process actually infers concepts (symbols) that represent entities in the physical world.Perception – action by a perceiver of executing the perception process
  • #51: Quality – property in the physical world that may be observed (i.e., accessible to the senses through stimuli); qualities inhere in entities.Entity – object, event, or situation in the physicalworld (not directly accessible to the senses and must be inferred* from observations of qualities and background knowledge). *Actually, concepts (symbols) are inferred that represent entities in the physical world.Background knowledge – set of relations between qualities and entities known to a perceiver
  • #52: Perception Cycle - a process that relates observers and perceivers; the observer communicates percepts to the perceiver, representing qualities that have been observed, and the perceiver communicates focus to the observer, representing qualities that should be observed. Percept – is a symbol that represents an observed quality (also sometimes called observation value).Focus – guides the observer towards only those qualities necessary for effective perception (in the form of a quality type).* Evaluation – in our experiments, we have shown that focus can reduce the number of observations necessary for perception by up to 50%.
  • #53: Integration of the threeontologies discussed above: sensor/observation ontology relating observers (or sensors) to observable qualities, perception ontology relating perceivers to perceivable (inferable) entities, and domain ontology relating qualities and entities in the physical world
  • #56: The top figure shows the results of executing the perception cycle for the 516 weather stations within a radius of 400 miles of the blizzard in Utah, and for each ordering of quality types. The horizontal axis represents the different orderings of observable qualities; p represents precipitation, w represents wind speed, and t represents temperature. The bottom figure shows the percentages of percepts generated during the perception cycle for different sets of observers (at different distances from the known blizzard) and for different orderings of quality types.
  • #58: Knoesis center recently declared a center of excellence by Ohio governor
  • #66: Get all sensors using well known location names – Problem to be solve
  • #68: Knoesis center recently declared a center of excellence by Ohio governor
  • #71: LOD Cloud is a way of sharing, exposing, and connecting pieces of data, information and knowledge on the Semantic Web using URI’s and RDFComprises of geographic, biological datasets etc
  • #73: Linked Data explodes
  • #76: Knoesis center recently declared a center of excellence by Ohio governor