Presentation is loading. Please wait.

Presentation is loading. Please wait.

US Army Corps of Engineers BUILDING STRONG ® Integration of Procedural and Semantic Knowledge with an Application to Hydrology Aaron Byrd David Tarboton.

Similar presentations


Presentation on theme: "US Army Corps of Engineers BUILDING STRONG ® Integration of Procedural and Semantic Knowledge with an Application to Hydrology Aaron Byrd David Tarboton."— Presentation transcript:

1

2 US Army Corps of Engineers BUILDING STRONG ® Integration of Procedural and Semantic Knowledge with an Application to Hydrology Aaron Byrd David Tarboton

3 BUILDING STRONG ® 23 June 2011 / Aaron Byrd Semantic and Procedural Knowledge Modeling Goal: Enable hydrologists to describe knowledge about the concepts, relationships between the concepts, and the procedures we use in our work in a form that allows the computer to reason over the knowledge, deduce consequent knowledge, and successfully complete tasks common to the field of hydrology, e.g. Configure models Process, assemble data Analyze data to deduce watershed properties 2

4 BUILDING STRONG ® 23 June 2011 / Aaron Byrd What is Semantic Knowledge Modeling? Modeling the meaning of information Meaning is expressed by relationships between concepts Expressed as a simple sentence: 3

5 BUILDING STRONG ® 23 June 2011 / Aaron Byrd How Do We Use Semantics? Describing relationships between concepts The water depth in the river at gage 1 is 3.7 meters 4

6 BUILDING STRONG ® Hydrologic Semantics 5

7 BUILDING STRONG ® The Logic Behind Semantics All about defining membership in sets Set Theory membership defined by attributes and properties Class Membership Type, Subclass, domain, range First Order Logic Symmetric Transitive Equivalence Restrictions Cardinality Existentiality 6

8 BUILDING STRONG ® Reasoning and Deduction 7 SubjectPredicateObjectSubjectPredicateObject HydrologicFluxrdf:typerdfs:ClassOverlandFlowMovesFromOverlandSurface HydrologicStoragerdf:typerdfs:ClassExfiltrationMovesFromGroundWater MovesFromrdfs:domainHydrologicFluxExfiltrationMovesToOverlandSurface MovesFromrdfs:rangeHydrologicStorageSubsurfaceDischargeMovesFromGroundWater MovesTordfs:domainHydrologicFluxSubsurfaceDischargeMovesToStreams MovesTordfs:rangeHydrologicStorageSubsurfaceDischargeMovesToOcean PrecipitationMovesFromAtmosphereStreamFlowMovesFromStreams PrecipitationMovesToOverlandSurfaceStreamFlowMovesToOcean InfiltrationMovesFromOverlandSurfaceEvaporationMovesFromOcean InfiltrationMovesToVadoseZoneEvaporationMovesFromStreams PercolationMovesFromVadoseZoneEvaporationMovesFromOverlandSurface PercolationMovesToGroundWaterEvaporationMovesFromVadose Zone InterflowMovesToStreamsEvaporationMovesToAtmosphere InterflowMovesFromVadoseZonehasSourceowl:InverseOfMovesTo OverlandFlowMovesToStreamshasLossowl:InverseOfMovesFrom What are the Hydrologic Storages? What sources does overland flow have?

9 BUILDING STRONG ® 23 June 2011 / Aaron Byrd What about other kinds of knowledge? Knowledge with an inherent sequence Steps to solve a problem What we make the computer do every day!!! 8 // first do the old cells for (i = 0; i < nRows; i++) { for (j = 0; j < nCols; j++) { newCells[(addNorth + i) * newCols + addWest + j] = cells[i * nCols + j]; } // new north section cells for (i = 0; i < addNorth; i++) { for (j = 0; j < newCols; j++) { newCells[i * newCols + j] = theSource.GetValue(newWest + ((double)j + 0.5) * cellsize, newNorth - ((double)i + 0.5) * cellsize); } // new west,east section cells for (i = 0; i < nRows; i++) { for (j = 0; j < addWest; j++) //west { newCells[(i + addNorth) * newCols + j] = theSource.GetValue(newWest + ((double)j + 0.5) * cellsize, newNorth - ((double)(i + addNorth) + 0.5) * cellsize); } …

10 BUILDING STRONG ® 23 June 2011 / Aaron Byrd Pulling it together: Functional Ontology API Integrates semantic models and procedural code How do you compute the property value of the attribute? Currently includes the following semantic logic Class/Subclass/Domain/Range Equivalence Inverse Currently includes the following code types Predicate functions Common functions User functions Secondary code Context Assessment 9

11 BUILDING STRONG ® 23 June 2011 / Aaron Byrd Interaction Between Procedural Knowledge and Semantic Knowledge Semantic -> Procedural Call functions to compute value when query returns the empty set Procedural -> Semantic Query against semantic knowledge base theOntology.FindMatchingSet(myTerrainGroup,td:hasComputable Data,?canCompute,results); Results stored in sets Can be used in semantic queries, accessible to code Can use set logic (Union, Intersection, Subtraction) 10

12 BUILDING STRONG ® 23 June 2011 / Aaron Byrd Example: Encapsulating Knowledge about TauDEM Functions 11

13 BUILDING STRONG ® 23 June 2011 / Aaron Byrd Adding Computational Semantics 12

14 BUILDING STRONG ® 23 June 2011 / Aaron Byrd Running the Functional Ontology 13

15 BUILDING STRONG ® Running the Functional Ontology 14 powerpointpowerpoint powerpointpowerpoint 7owe3p9int7owe3p9int pow4r2ointpow4r2oint powerpointpowerpoint awesomeawesome awesomeawesome powerpointpowerpoint power8ointpower8oint powerpointpowerpoint powe3pointpowe3point powerpointpowerpoint powerpointpowerpoint powerpointpowerpoint awesomeawesome awesomeawesome awesomea25gbgarytajsawesomea25gbgarytajs awesomeawesome awesomeawesome.. : ¦

16 BUILDING STRONG ® Running the Functional Ontology: Queries 15

17 BUILDING STRONG ® Running the Functional Ontology: User Functions 16

18 BUILDING STRONG ® Running the Functional Ontology: Functional Queries 17

19 BUILDING STRONG ® Running the Functional Ontology: Functional Queries 18

20 BUILDING STRONG ® 23 June 2011 / Aaron Byrd Semantic and Procedural Knowledge Modeling Goal: Enable hydrologists to describe knowledge about the concepts, relationships between the concepts, and the procedures we use in our work in a form that allows the computer to reason over the knowledge, deduce consequent knowledge, and successfully complete tasks common to the field of hydrology

21 BUILDING STRONG ® 23 June 2011 / Aaron Byrd Conclusions Semantic modeling can capture knowledge in a form that enables reasoning engines to deduce consequent knowledge Adding procedural knowledge and execution to a semantic engine enables the capture and use of a large body of knowledge that is difficult or impossible to capture solely in a semantic model Using a coupled semantic-procedural reasoning engine enables us to capture many kinds of hydrologic knowledge in a fashion the places our business logic in a knowledge base rather than hard-coded in a program.


Download ppt "US Army Corps of Engineers BUILDING STRONG ® Integration of Procedural and Semantic Knowledge with an Application to Hydrology Aaron Byrd David Tarboton."

Similar presentations


Ads by Google