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Spatial Semantics for Better Interoperability and Analysis: ,[object Object],Challenges And Experiences In Building Semantically Rich Applications In Web 3.0,[object Object],(Keynote at the 3rd Annual Spatial Ontology Community of Practice Workshop (SOCoP), USGS Reston, VA, December 03, 2010),[object Object],Amit Sheth,[object Object],LexisNexis Ohio Eminent Scholar,[object Object],Ohio Center of Excellence in Knowledge-enabled Computing – Kno.e.sis,[object Object],Wright State University, Dayton, OH ,[object Object],http://knoesis.org,[object Object],Semantic Provenance: Trusted Biomedical Data Integration,[object Object],Thanks: Cory Henson, Prateek Jain,[object Object],& Kno.e.sis Team. Ack: NSF and other,[object Object],Funding sources.,[object Object]
Semantics as core enabler, enhancer @ Kno.e.sis,[object Object],15 faculty,[object Object],45+ PhD students & post-docs,[object Object],Excellent Industry collaborations (MSFT, GOOG, IBM, Yahoo!, HP),[object Object],Well funded,[object Object],Multidisciplinary,[object Object],Exceptional Graduates,[object Object],2,[object Object]
Web (and associated computing) evolving,[object Object],Web ofpeople, Sensor Web,[object Object],   - social networks, user-created casual content,[object Object],   - 40 billion sensors,[object Object],Web of resources,[object Object],    - data, service, data, mashups,[object Object],    - 4 billion mobilecomputing,[object Object],Web of databases,[object Object],   - dynamically generated pages,[object Object],   - web query interfaces,[object Object],Web of pages,[object Object],   - text, manually created links,[object Object],   - extensive navigation,[object Object],Computing for Human Experience,[object Object],Enhanced Experience,,[object Object],Tech assimilated in life,[object Object],http://bit.ly/HumanExperience,[object Object],Web as an oracle / assistant / partner,[object Object],   - “ask the Web”: using semantics to leveragetext + data + services,[object Object],2007,[object Object],Situations,,[object Object],Events,[object Object],Semantic TechnologyUsed,[object Object],Objects,[object Object],Web 3.0,[object Object],Web 2.0,[object Object],Web 1.0,[object Object],Patterns,[object Object],Keywords,[object Object],1997,[object Object]
Variety & Growth of Data,[object Object],Variety/Heterogeneity,[object Object],Many intelligent applications that involve fusion and integrated analysis of wide variety of data,[object Object],Web 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…,[object Object],Exponential growth for each data: e.g. Mobile Data,[object Object],2009: 1 Exabyte (EB),[object Object],2010 US alone: 40+ EB. ,[object Object],Estimate of 2016-17 (Worldwide): 1 Zettabyte (ZB) or 1000 Exabytes. ,[object Object],(Managing Growth & Profits in the Yottabytes Era, Chetan Sharma Consulting, 2009). ,[object Object]
A large class of Web 3.0 applications…,[object Object],utilize larger amount of historical and recent/real-time data of various types from multiple sources (lot of data has spatial property),[object Object],not only search, but analysis of or insight from data – that is applications are more “intelligent”,[object Object],This calls for semantics: spatial, temporal, thematic components;  background knowledge,[object Object],This talk: spatial semantics as a key component in building many Web 3.0 applications,[object Object]
A Challenging Example Query,[object Object],What schools in Ohio should now be closed due to inclement weather?,[object Object],Need domain ontologies and rules to describe type of inclement weather and severity.,[object Object],Integrationof technologies needed to answer query,[object Object],Spatial Aggregation,[object Object],Semantic  Sensor Web,[object Object],Machine Perception,[object Object],Linked Sensor Data,[object Object],Analysis of Streaming Real-Time Data,[object Object],6,[object Object]
Technology 1Spatial Aggregation,[object Object],[object Object]
 What weather sensors are near each of the school?7,[object Object]
Spatial Aggregation,[object Object],Utilizes partonomy in order to aggregate spatial regions,[object Object],To query over spatial regions at different levels of granularity,[object Object],[object Object]
Query represents “high-level” state (school in state)8,[object Object]
Increased Availability of Spatial Info,[object Object],9,[object Object]
Accessing Can Be Difficult,[object Object],10,[object Object]
Must Ask for Information the “Right” Way,[object Object],11,[object Object]
Why is This Issue Relevant?,[object Object],Spatial data becoming more significant day by day.,[object Object],Crucial for multitude of applications:,[object Object],Social Networks like Twitter, Facebook…,[object Object],GPS,[object Object], Military,[object Object], Location Aware Services: Four Square Check-In,[object Object], weather data…,[object Object],[object Object],Twitter Feeds, Facebook posts.,[object Object],Naïve users contribute and correct spatial data too which can lead to discrepancies in data representation.,[object Object],E.g. Geonames, Open Street Maps,[object Object],12,[object Object]
What We Want,[object Object], Automatically align conceptual mismatches ,[object Object],User’s Query,[object Object],Spatial Information of Interest,[object Object],Semantic Operators,[object Object],13,[object Object]
What is the Problem?,[object Object],[object Object]
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,[object Object]
What Needs to be Done?,[object Object],Reduce users’ burden of having to know how information of interest is represented and structured to enable access by broad population.,[object Object],Resolve mismatches between a query and information of interest due to differences in granularity to improve recall of relevant information. ,[object Object],Resolve ambiguous relationships between entities based on natural language to reduce the amount of wrong information retrieved.,[object Object],15,[object Object]
Existing Mechanism for Querying RDF,[object Object],SPARQL,[object Object],Regular Expression Based Querying Approaches,[object Object],16,[object Object]
Common Query Testing All Approaches,[object Object],“Find Schools Located in the State of Ohio”,[object Object],17,[object Object]
In a Perfect Scenario,[object Object],parent feature,[object Object],School,[object Object],Ohio,[object Object],18,[object Object]
In a Not so Perfect Scenario,[object Object],County,[object Object],parent feature,[object Object],School,[object Object],Ohio,[object Object],parent feature,[object Object],19,[object Object]
County,[object Object],parent feature,[object Object],Ohio,[object Object],parent feature,[object Object],School,[object Object],And Finally..,[object Object],Exchange students ,[object Object],parent feature,[object Object],School,[object Object],Indiana,[object Object],20,[object Object]
Proposed Approach,[object Object],Define 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. ,[object Object],Define transformation rules for operators based on work by Winston’s taxonomy of part-whole relations.,[object Object],Rule based approach allows applicability in different domains with appropriate modifications.,[object Object],PartonomicalRelationship Based Query Rewriting System (PARQ) implements this approach.,[object Object],21,[object Object]
Meta Rules for Winston’s Categories ,[object Object],Transitivity,[object Object],(a φ-part of b)	(b φ-part of c)	(a φ-part of c),[object Object],Dayton place-part of Ohio,[object Object],Ohio place-part of US,[object Object],Dayton place-part of US,[object Object],Overlap,[object Object],(a place-part of b)	(a place-part of b)	(b overlaps c),[object Object],Sri Lank place-part of Indian Ocean,[object Object],Sri Lank place-part of Bay of Bengal,[object Object],Indian Ocean overlaps with Bay of Bengal,[object Object],Spatial Inclusion,[object Object],(a place-part of b)	(a place-part of b)	(b overlaps c),[object Object],White House instance of Building,[object Object],Barack is in ,[object Object],the White House,[object Object],Barack is,[object Object],In the building,[object Object],22,[object Object]
Slight and Severe Mismatch,[object Object],SELECT ?school,[object Object],WHERE 	{,[object Object],	?state	geo:featureClassgeo:A	?schools	geo:featureClassgeo:S,[object Object],	?state	geo:name	"Ohio“,[object Object],	     ?schools	geo:parentFeature	?state ,[object Object],},[object Object],Query Re-Writer,[object Object],SELECT ?school,[object Object],WHERE	{,[object Object],	?state        geo:featureClassgeo:A	?schools    geo:featureClassgeo:S,[object Object],	?state        	geo:name               	"Ohio“,[object Object],	?school    geo:parentFeature    	?county ,[object Object],	?county	geo:parentFeature    	?state,[object Object],	},[object Object],23,[object Object]
Where Do We Stand With All Mechanisms..,[object Object],24,[object Object]
Evaluation,[object Object],Performed on publicly available datasets (Geonames and British Ordnance Survey Ontology),[object Object],Utilized 120 questions  from National Geographic Bee and 46 questions from trivia related to British Administrative Geography,[object Object],Questions serialized into SPARQL Queries by 4 human respondents unfamiliar with ontology,[object Object],Performance of PARQ compared with PSPARQL and SPARQL,[object Object],25,[object Object]
Sample Queries,[object Object],“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?”,[object Object],“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.”,[object Object],26,[object Object]
PARQ  - vs -  SPARQL,[object Object],27,[object Object]
PARQ  - vs -  PSPARQL,[object Object],Comparison for National  Geographic Bee over Geonames,[object Object],Comparison for British Admin. Trivia over Ordnance Survey Dataset,[object Object],28,[object Object]
Spatial Aggregation Conclusion,[object Object],Query engines expect users to know the dataset structure and pose well formed queries,[object Object],Query engines ignore semantic relations between query terms,[object Object],Need to exploit semantic relations between concepts  for processing queries,[object Object],Need to provide systems with behind the scenes rewrite of queries to remove burden of knowing structure of data,[object Object],29,[object Object]
Technology 2,[object Object],Semantic Sensor Web (SSW),[object Object],[object Object]
 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,[object Object]
Semantic Sensor Web,[object Object],Utilizes ontologies to represent and analyze heterogeneous sensor data,[object Object],[object Object]
Spatial ontology
Temporal ontology
Domain ontologies (i.e., weather ontology)Generates abstractions (that matter to human decision making) over sensor data,[object Object],[object Object],[object Object]
Sensors are now ubiquitous, 	and constantly generating observations about our world,[object Object],33,[object Object]
However, these systems are often stovepiped,,[object Object],	with strong tie between sensor network and application,[object Object],34,[object Object]
We want to set this data free,[object Object],35,[object Object]
With freedom comes new responsibilities ….,[object Object],36,[object Object]
	Web Services,[object Object],		- OGC Sensor Web Enablement (SWE),[object Object],1) How to discover, access and search the data?,[object Object],37,[object Object]
when it comes from many different sources?,[object Object],	Shared knowledge models, or Ontologies,[object Object],		- syntactic models – XML (SWE),[object Object],		- semantic models – OWL/RDF (W3C SSN-XG),[object Object],2) How to integrate this data together,[object Object],38,[object Object]
The SSN-XG Deliverables ,[object Object],[object Object]
Illustrate the relationship to OGC Sensor Web Enablement standards
Semantic annotation of OGC Sensor Web Enablement standards39,[object Object]
3) Make streaming numerical sensor data meaningful to web applications and naïve users?,[object Object],Symbols more meaningful than numbers,[object Object],		- analysis and reasoning (understanding through perception),[object Object]
Overall Architecture,[object Object]
SSW demo with Mesowest data,[object Object],http://knoesis.org/projects/sensorweb/demos/semsos_mesowest/ssos_demo.htm,[object Object]
Technology 3,[object Object],Active Machine Perception,[object Object],[object Object],43,[object Object]
Machine Perception,[object Object],Task of extracting meaning from sensor data,[object Object],Perception is the act of choosing from alternative explanations for a set of observations (Intellego Perception),[object Object],Perception is a active, cyclical process of explaining observations by actively seeking – or focusing on – additional information (Active Perception),[object Object],Active Perception cycle is driven by prior knowledge,[object Object],44,[object Object]
Goal to Obtain,[object Object],Awareness of the Situation,[object Object],Web,[object Object],observe,[object Object],perceive,[object Object],“Real-World”,[object Object],45,[object Object]
Formal Theory of Machine Perception,[object Object],Specification,[object Object],Implementation,[object Object],Evaluation,[object Object],Ontology of Perception: A Semantic Web Approach to Enhance Machine Perception (Technical Report, Sept. 2010),[object Object],46,[object Object]
Enable Situation Awareness on Web,[object Object],Must utilize abstractionscapable of representing observations and perceptions generated by either people or machines.,[object Object],Web,[object Object],observe,[object Object],perceive,[object Object],“Real-World”,[object Object],47,[object Object]
Observation of Qualities,[object Object],Both people and machines are capable of observing qualities, such as redness.,[object Object],observes,[object Object],Observer,[object Object],Quality,[object Object],Formally described in a sensor/observation ontology,[object Object],48,[object Object]
Perception of Entities,[object Object],Both people and machines are also capable of perceiving entities, such as apples,[object Object],perceives,[object Object],Perceiver,[object Object],Entity,[object Object],* Formally described in a perception ontology,[object Object],49,[object Object]
Background Knowledge,[object Object],Ability 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. ,[object Object],Quality,[object Object],inheres in,[object Object],Entity,[object Object],Formally described in a domain ontology,[object Object],50,[object Object]
Perception Cycle,[object Object],The ability to perceive efficiently is afforded through the cyclical exchange of information between observers and perceivers. ,[object Object],Observer,[object Object],sends,[object Object],focus,[object Object],sends ,[object Object],percept,[object Object],Perceiver,[object Object],Traditionally called the Perception Cycle (or Active Perception),[object Object],51,[object Object]
Integrated Perception Cycle,[object Object],Integrated together, we have an abstract model – capable of situation awareness – relating observers, perceivers, and background knowledge.,[object Object],observes,[object Object],Observer,[object Object],Quality,[object Object],sends ,[object Object],percept,[object Object],sends,[object Object],focus,[object Object],inheres in,[object Object],perceives,[object Object],Perceiver,[object Object],Entity,[object Object],52,[object Object]
Specification of Perception Cycle ,[object Object],(in set theory),[object Object],53,[object Object]
Implementation of Perception Cycle,[object Object],54,[object Object],54,[object Object]
Evaluation of Perception Cycle,[object Object],We demonstrated 50% savings in resource requirementsby utilizing background knowledge within the Perception Cycle,[object Object],55,[object Object],55,[object Object]
Trusted Perception Cycle Demo,[object Object],http://www.youtube.com/watch?v=lTxzghCjGgU,[object Object],http://knoesis.org/projects/sensorweb/demos/trusted_perception_cycle/,[object Object]
Technology 4,[object Object],Linked Sensor Data,[object Object],[object Object]
 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,[object Object]
Linked Sensor Data,[object Object],Knowledge/representations from SSW are accessible on LOD,[object Object],LinkedSensorData,[object Object],[object Object]
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,[object Object]
Linked Open Data,[object Object],Community-led effort to create openly accessible, and interlinked, semantic (RDF) data on the Web,[object Object],59,[object Object]

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

Notes de l'éditeur

  1. Knoesis center recently declared a center of excellence by Ohio governor: http://bit.ly/coe-k
  2. Knoesis center recently declared a center of excellence by Ohio governor
  3. A mechanism to improve access to spatial information by allowing users
  4. Knoesis center recently declared a center of excellence by Ohio governor
  5. Making sense of data from many sources in many form is challenging
  6. Knoesis center recently declared a center of excellence by Ohio governor
  7. 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
  8. 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
  9. 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
  10. 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%.
  11. 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
  12. 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.
  13. Knoesis center recently declared a center of excellence by Ohio governor
  14. Get all sensors using well known location names – Problem to be solve
  15. Knoesis center recently declared a center of excellence by Ohio governor
  16. 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
  17. Linked Data explodes
  18. Knoesis center recently declared a center of excellence by Ohio governor