Conversational Case Base Conversational Case Base Recommender Systems for Recommender Systems for

Metadata DiscoveryMetadata Discovery

Mehmet S. Aktas, Marlon Pierce, Mehmet S. Aktas, Marlon Pierce, Geoffrey Fox and David LeakeGeoffrey Fox and David Leake

Indiana UniversityIndiana University

SSolid olid EEarth arth RResearch esearch VVirtual irtual OObservatory bservatory GridGrid ( (SERVOGridSERVOGrid)) SERVOGridSERVOGrid is a NASA is a NASA

project to project to integrateintegrate historical, measured, historical, measured, and calculated and calculated earthquake dataearthquake data (GPS, (GPS, Seismicity, Fault) Seismicity, Fault) with with simulation codes.simulation codes.

SERVOGrid resourcesSERVOGrid resources located at various located at various institutions institutions across the across the country.country.

# of resources# of resources, services , services and their usage and their usage frequency frequency expected to expected to grow quickly.grow quickly.

Characteristics of Computing for Characteristics of Computing for Solid Earth ScienceSolid Earth Science

Widely distributed datasetsWidely distributed datasets in various formats in various formats• GPS, Fault data, Seismic data sets, InSAR satellite dataGPS, Fault data, Seismic data sets, InSAR satellite data• Many available in state of art tar files that can be FTP’dMany available in state of art tar files that can be FTP’d

Distributed models and expertiseDistributed models and expertise• Lots of codes with different regions of validity, ranging Lots of codes with different regions of validity, ranging

from cellular automata to finite element to data mining from cellular automata to finite element to data mining applications (HMM) applications (HMM)

• Some codes also have export or IP restrictionsSome codes also have export or IP restrictions• Other codes are highly specialized to their deployment Other codes are highly specialized to their deployment

environments.environments. Decomposable problemsDecomposable problems requiring requiring

interoperability for linking full modelsinteroperability for linking full models• The fidelity of your fault modeling can vary considerablyThe fidelity of your fault modeling can vary considerably• Link codes (through data) to support multiple scalesLink codes (through data) to support multiple scales

SERVOGrid ApplicationsSERVOGrid Applications Codes range from simple “rough estimate” codes to Codes range from simple “rough estimate” codes to

parallel, high performance applications.parallel, high performance applications.• DislocDisloc: handles multiple arbitrarily dipping dislocations : handles multiple arbitrarily dipping dislocations

(faults) in an elastic half-space.(faults) in an elastic half-space.• SimplexSimplex: inverts surface geodetic displacements for fault : inverts surface geodetic displacements for fault

parameters using simulated annealing downhill residual parameters using simulated annealing downhill residual minimization. minimization.

• GeoFESTGeoFEST: Three-dimensional viscoelastic finite element : Three-dimensional viscoelastic finite element model for calculating nodal displacements and tractions. model for calculating nodal displacements and tractions. Allows for realistic fault geometry and characteristics, Allows for realistic fault geometry and characteristics, material properties, and body forces. material properties, and body forces.

• VirtualVirtual CaliforniaCalifornia: Program to simulate interactions : Program to simulate interactions between vertical strike-slip faults using an elastic layer over between vertical strike-slip faults using an elastic layer over a viscoelastic half-space a viscoelastic half-space

• RDAHMMRDAHMM: Time series analysis program based on Hidden : Time series analysis program based on Hidden Markov Modeling. Produces feature vectors and probabilities Markov Modeling. Produces feature vectors and probabilities for transitioning from one class to another. for transitioning from one class to another.

Preprocessors, mesh generators: Preprocessors, mesh generators: AKIRA suiteAKIRA suite Visualization tools: Visualization tools: RIVARIVA, , GMT, IDLGMT, IDL

MotivationMotivation Most fundamental challenge is just making Most fundamental challenge is just making

these codes useable for other researchers.these codes useable for other researchers.

And hooking these codes to data sourcesAnd hooking these codes to data sources

First step is to express resources with First step is to express resources with descriptive metadatadescriptive metadata

Then explore intelligent retrieval Then explore intelligent retrieval mechanisms to make these resources mechanisms to make these resources availableavailable

SERVOGrid Ontology Overview SERVOGrid Ontology Overview We have a collection of codes, visualization tools, We have a collection of codes, visualization tools,

computing resources, and data sets that we want computing resources, and data sets that we want to combine in an ontology.to combine in an ontology.

Ontology instances can then be built to describe Ontology instances can then be built to describe specific resources.specific resources.

After we have built instances, we can pose After we have built instances, we can pose queries on the data to retrieve values.queries on the data to retrieve values.• Values may be structured, so we can do “stepped” Values may be structured, so we can do “stepped”

queries.queries.

We thus need to start by grouping together We thus need to start by grouping together related resources.related resources.

An Instance for Disloc codeAn Instance for Disloc code<rdf:RDF xmlns:rdf='http://www.w3c.org/1999/02/22-rdf-syntax-ns#'<rdf:RDF xmlns:rdf='http://www.w3c.org/1999/02/22-rdf-syntax-ns#'

xmlns:rdfs='http://www.w3c.org/2000/01/rdf-schema#'xmlns:rdfs='http://www.w3c.org/2000/01/rdf-schema#' xmlns:servo='http://www.servogrid.org/schemas/SERVOGridOntology#'xmlns:servo='http://www.servogrid.org/schemas/SERVOGridOntology#' xmlns:dc="http://purl.org/dc/elements/1.0/">xmlns:dc="http://purl.org/dc/elements/1.0/">

<rdf:Description rdf:ID="Disloc"><rdf:Description rdf:ID="Disloc"><rdf:type <rdf:type rdf:resource="http://www.servogrid.org/schemas/servoOntology#Applicationrdf:resource="http://www.servogrid.org/schemas/servoOntology#ApplicationCode"/>Code"/><dc:creator>A. Donnellan</dc:creator><dc:creator>A. Donnellan</dc:creator><servo:installedOn <servo:installedOn rdf:resource="http://www.servogrid.org/instances/ComputeResources/rdf:resource="http://www.servogrid.org/instances/ComputeResources/Grids"/>Grids"/><servo:takesInputData <servo:takesInputData rdf:resource="http://www.servogrid.org/instances/data/Faults"/>rdf:resource="http://www.servogrid.org/instances/data/Faults"/><servo:createsOuputData <servo:createsOuputData rdf:resource="http://www.servogrid.org/instances/data/SurfaceStress"/>rdf:resource="http://www.servogrid.org/instances/data/SurfaceStress"/>

</rdf:Description></rdf:Description></rdf:RDF></rdf:RDF>

From SW Representation to Case From SW Representation to Case Base Reasoning (CBR)Base Reasoning (CBR)

Developing new tools, applications and Developing new tools, applications and architectures on top of the Semantic Web architectures on top of the Semantic Web is the real challenge.is the real challenge.• Can we ensure consistency and correctness in Can we ensure consistency and correctness in

the presentation of information??the presentation of information??

AI techniques could be considered as basis AI techniques could be considered as basis for a resource recommender system. for a resource recommender system.

CBR is most suitable AI technique for CBR is most suitable AI technique for SERVOGrid domain.SERVOGrid domain.

What is Case-Based Reasoning?What is Case-Based Reasoning?(CBR in a Nutshell)(CBR in a Nutshell)

CBR is reasoning by rememberingCBR is reasoning by remembering

In CBR, recommendations are made by In CBR, recommendations are made by doing reasoning from current set of casesdoing reasoning from current set of cases

Classification CBRClassification CBR• when a similar problem description is entered when a similar problem description is entered

most similar cases are suggested (by most similar cases are suggested (by comparing and contrasting problem description comparing and contrasting problem description with current set of cases) to the user as resultswith current set of cases) to the user as results

Conversational CBR (CCBR)Conversational CBR (CCBR)(CCBR in a Nutshell)(CCBR in a Nutshell)

CCBR is a type of CBR that relies on question-CCBR is a type of CBR that relies on question-answer sessions to recommend most similar cases.answer sessions to recommend most similar cases.

User interacts with the system to fill in the gaps to User interacts with the system to fill in the gaps to retrieve right casesretrieve right cases

System responds with ranked cases and questions System responds with ranked cases and questions at each stepat each step

Question-answer-ranking cycle continues until Question-answer-ranking cycle continues until success or failuresuccess or failure• success: if user finds an answer to his querysuccess: if user finds an answer to his query• failure: if no satisfactory case is foundfailure: if no satisfactory case is found

What is a Case?What is a Case?(CCBR Case in SERVOGrid)(CCBR Case in SERVOGrid)

A case is composed of:A case is composed of:• problem description: metadata concerning problem description: metadata concerning

desired characteristics of a SERVOGrid desired characteristics of a SERVOGrid resource, e.g., RDF triples describing a resource, e.g., RDF triples describing a resourceresource

• solution: pointer to a resource described by solution: pointer to a resource described by metadata in problem descriptionmetadata in problem description

A Casebase is library of cases generated A Casebase is library of cases generated from file store of RDF files each from file store of RDF files each representing a case.representing a case.

CCBR CASE

Problem SolutionRDF Triple

= (Subject, Predicate, Object)

CCBR Case with RDF CCBR Case with RDF RepresentationRepresentation

RDF Triple

RDF Triple

CCBR Recommender SystemCCBR Recommender System Ranking of the casesRanking of the cases

• Cases will be ranked based on their consistent triple numbers.Cases will be ranked based on their consistent triple numbers.

• If the case has a matching triple, it will have higher ranking.If the case has a matching triple, it will have higher ranking.

• If the case does not have the entered triple, its ranking won’t If the case does not have the entered triple, its ranking won’t change, unless user wants the cases which don’t have this change, unless user wants the cases which don’t have this triple.triple.

Ranking of the questionsRanking of the questions• Ranking can be based on (property, property value) Ranking can be based on (property, property value)

appearance # in the triples stored in the case base. appearance # in the triples stored in the case base.

• System must recommend good starting points for user System must recommend good starting points for user specification of servoObject class properties.specification of servoObject class properties.

CCBR CASEBASE

CaseFeature 1Feature 2Feature 5

Case = <Problem, Solution>

Feature 1Feature 2Feature 3Feature 4

A Case from CASEBASE

Query Case

IF ((A.Feature1.Solution = B.Feature1.Solution) &

(A.Feature2.Solution = B.Feature2.Solution))

THEN Consistency # = 2

A B

CCBR Recommender SystemCCBR Recommender System

Recap: SERVOGrid Case Base Recap: SERVOGrid Case Base Recommender SystemRecommender System

goal: locating resources in a large scale goal: locating resources in a large scale environment (SERVOGrid project)environment (SERVOGrid project)

approach:approach:• SERVOGrid ontology instances (metadata) to SERVOGrid ontology instances (metadata) to

describe resourcesdescribe resources• Recommender system to aid metadata Recommender system to aid metadata

discoverydiscovery• Conversational CBR with SW markup languages Conversational CBR with SW markup languages

providing standard form for case providing standard form for case representationrepresentation

More InformationMore Information SERVOGrid/QuakeSim:SERVOGrid/QuakeSim:

• http://quakesim.jpl.nasa.gov/http://quakesim.jpl.nasa.gov/

SERVOGrid Recommender Systems project: SERVOGrid Recommender Systems project: • http://tambora.ucs.indiana.edu/~maktas/servo/project.hthttp://tambora.ucs.indiana.edu/~maktas/servo/project.ht

mlml

SERVOGrid Recommender Systems demo:SERVOGrid Recommender Systems demo:• http://ripvanwinkle.ucs.indiana.edu:4780/cbr/http://ripvanwinkle.ucs.indiana.edu:4780/cbr/

selection.jspselection.jsp

Publications:Publications:• http://grids.ucs.indiana.edu/ptliupages/publications/http://grids.ucs.indiana.edu/ptliupages/publications/

Questions/CommentsQuestions/Comments

Any questions and/or comments?Any questions and/or comments?

Thanks!Thanks!