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Integration of Procedural and Semantic Knowledge with an Application to Hydrology

Integration of Procedural and Semantic Knowledge with an Application to Hydrology. Aaron Byrd David Tarboton. Semantic and Procedural Knowledge Modeling. Goal: Enable hydrologists to describe knowledge about the concepts, relationships between the concepts, and the procedures

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Integration of Procedural and Semantic Knowledge with an Application to Hydrology

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  1. Integration of Procedural and Semantic Knowledge with an Application to Hydrology Aaron Byrd David Tarboton

  2. 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

  3. What is Semantic Knowledge Modeling? • Modeling the meaning of information • Meaning is expressed by relationships between concepts • Expressed as a simple sentence: • <Concept 1> <Relationship> <Concept 2> • <Thing> <Attribute> <Property> • <Subject> <Predicate> <Object>

  4. How Do We Use Semantics? • Describing relationships between concepts • “The water depth in the river at gage 1 is 3.7 meters” • <River> <has Measuring Location> <Gage 1> • <River> <has Property> <Water Depth> • <Water Depth> <has Measurement> <3.7> • <Water Depth> <has Units> <meters>

  5. Hydrologic Semantics

  6. 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

  7. Reasoning and Deduction What are the Hydrologic Storages? What sources does overland flow have?

  8. What about other kinds of knowledge? • Knowledge with an inherent sequence • Steps to solve a problem • What we make the computer do every day!!! // 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);         } …

  9. 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

  10. Interaction Between Procedural Knowledge and Semantic Knowledge • Semantic -> Procedural • Call functions to compute value when query returns the empty set • <myTerrainGroup> <td:hasComputableData> <?canCompute> • Procedural -> Semantic • Query against semantic knowledge base • theOntology.FindMatchingSet(“myTerrainGroup”,”td:hasComputableData”,”?canCompute”,results); • Results stored in sets • Can be used in semantic queries, accessible to code • Can use set logic (Union, Intersection, Subtraction)

  11. Example: Encapsulating Knowledge about TauDEM Functions

  12. Adding Computational Semantics

  13. Running the Functional Ontology

  14. Running the Functional Ontology ;alsid co98390239-wef p o w e r p o i n t awesome 0 1 0 1 0 1 1 awesome a 2 5 g b g a r yt a j s kdiielkn ad asd you are the bonb p o w e 3 p o i n t diemmxco do do 7 o w e 3 p 9 i n t p o w e r p o i n t p o w e r p o i n t kldolkaciemd p o w e r p o i n t awesome p o w e r p o i n t ¦ awesome awesome p o w e r p o i n t awesome p o w e r 8 o i n t awesome p o w e r p o i n t p o w 4 r 2 o i n t p o w e r p o i n t . . :

  15. Running the Functional Ontology: Queries

  16. Running the Functional Ontology: User Functions

  17. Running the Functional Ontology: Functional Queries

  18. Running the Functional Ontology: Functional Queries

  19. 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

  20. 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.

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