1. Conversational Case Base Recommender Systems for Metadata Discovery Mehmet S. Aktas, Marlon Pierce, Geoffrey Fox and David Leake Indiana University

2. Solid Earth Research Virtual Observatory Grid (SERVOGrid) SERVOGrid is a NASA project to integrate historical, measured, and calculated earthquake data (GPS, Seismicity, Fault) with simulation codes. SERVOGrid is a NASA project to integrate historical, measured, and calculated earthquake data (GPS, Seismicity, Fault) with simulation codes. SERVOGrid resources located at various institutions across the country. SERVOGrid resources located at various institutions across the country. # of resources, services and their usage frequency expected to grow quickly. # of resources, services and their usage frequency expected to grow quickly.

3. Characteristics of Computing for Solid Earth Science Widely distributed datasets in various formats Widely distributed datasets 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 expertise Distributed models and expertise Lots of codes with different regions of validity, ranging from cellular automata to finite element to data mining applications (HMM)Lots of codes with different regions of validity, ranging from cellular automata to finite element to data mining 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 environments.Other codes are highly specialized to their deployment environments. Decomposable problems requiring interoperability for linking full models Decomposable problems requiring interoperability 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

4. SERVOGrid Applications Codes range from simple “rough estimate” codes to parallel, high performance applications. Codes range from simple “rough estimate” codes to parallel, high performance applications. Disloc: handles multiple arbitrarily dipping dislocations (faults) in an elastic half-space.Disloc: handles multiple arbitrarily dipping dislocations (faults) in an elastic half-space. Simplex: inverts surface geodetic displacements for fault parameters using simulated annealing downhill residual minimization.Simplex: inverts surface geodetic displacements for fault parameters using simulated annealing downhill residual minimization. GeoFEST: Three-dimensional viscoelastic finite element model for calculating nodal displacements and tractions. Allows for realistic fault geometry and characteristics, material properties, and body forces.GeoFEST: Three-dimensional viscoelastic finite element model for calculating nodal displacements and tractions. Allows for realistic fault geometry and characteristics, material properties, and body forces. Virtual California: Program to simulate interactions between vertical strike-slip faults using an elastic layer over a viscoelastic half-spaceVirtual California: Program to simulate interactions between vertical strike-slip faults using an elastic layer over a viscoelastic half-space RDAHMM: Time series analysis program based on Hidden Markov Modeling. Produces feature vectors and probabilities for transitioning from one class to another.RDAHMM: Time series analysis program based on Hidden Markov Modeling. Produces feature vectors and probabilities for transitioning from one class to another. Preprocessors, mesh generators: AKIRA suite Preprocessors, mesh generators: AKIRA suite Visualization tools: RIVA, GMT, IDL Visualization tools: RIVA, GMT, IDL

5. Motivation Most fundamental challenge is just making these codes useable for other researchers. Most fundamental challenge is just making these codes useable for other researchers. And hooking these codes to data sources And hooking these codes to data sources First step is to express resources with descriptive metadata First step is to express resources with descriptive metadata Then explore intelligent retrieval mechanisms to make these resources available Then explore intelligent retrieval mechanisms to make these resources available

6. SERVOGrid Ontology Overview We have a collection of codes, visualization tools, computing resources, and data sets that we want to combine in an ontology. We have a collection of codes, visualization tools, computing resources, and data sets that we want to combine in an ontology. Ontology instances can then be built to describe specific resources. Ontology instances can then be built to describe specific resources. After we have built instances, we can pose queries on the data to retrieve values. After we have built instances, we can pose queries on the data to retrieve values. Values may be structured, so we can do “stepped” queries.Values may be structured, so we can do “stepped” queries. We thus need to start by grouping together related resources. We thus need to start by grouping together related resources.

10. An Instance for Disloc code <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/"> A. Donnellan A. Donnellan </rdf:Description></rdf:RDF>

11. From SW Representation to Case Base Reasoning (CBR) Developing new tools, applications and architectures on top of the Semantic Web is the real challenge. Developing new tools, applications and architectures on top of the Semantic Web is the real challenge. Can we ensure consistency and correctness in the presentation of information??Can we ensure consistency and correctness in the presentation of information?? AI techniques could be considered as basis for a resource recommender system. AI techniques could be considered as basis for a resource recommender system. CBR is most suitable AI technique for SERVOGrid domain. CBR is most suitable AI technique for SERVOGrid domain.

12. What is Case-Based Reasoning? (CBR in a Nutshell) CBR is reasoning by remembering CBR is reasoning by remembering In CBR, recommendations are made by doing reasoning from current set of cases In CBR, recommendations are made by doing reasoning from current set of cases Classification CBR Classification CBR when a similar problem description is entered most similar cases are suggested (by comparing and contrasting problem description with current set of cases) to the user as resultswhen a similar problem description is entered most similar cases are suggested (by comparing and contrasting problem description with current set of cases) to the user as results

13. Conversational CBR (CCBR) (CCBR in a Nutshell) CCBR is a type of CBR that relies on question- answer sessions to recommend most similar cases. CCBR is a type of CBR that relies on question- answer sessions to recommend most similar cases. User interacts with the system to fill in the gaps to retrieve right cases User interacts with the system to fill in the gaps to retrieve right cases System responds with ranked cases and questions at each step System responds with ranked cases and questions at each step Question-answer-ranking cycle continues until success or failure Question-answer-ranking cycle continues until success 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

14. What is a Case? (CCBR Case in SERVOGrid) A case is composed of: A case is composed of: problem description: metadata concerning desired characteristics of a SERVOGrid resource, e.g., RDF triples describing a resourceproblem description: metadata concerning desired characteristics of a SERVOGrid resource, e.g., RDF triples describing a resource solution: pointer to a resource described by metadata in problem descriptionsolution: pointer to a resource described by metadata in problem description A Casebase is library of cases generated from file store of RDF files each representing a case. A Casebase is library of cases generated from file store of RDF files each representing a case.

15. CCBR CASE Problem Solution RDF Triple =(Subject, Predicate, Object) CCBR Case with RDF Representation RDF Triple

16. CCBR Recommender System Ranking of the cases Ranking 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 change, unless user wants the cases which don’t have this triple.If the case does not have the entered triple, its ranking won’t change, unless user wants the cases which don’t have this triple. Ranking of the questions Ranking of the questions Ranking can be based on (property, property value) appearance # in the triples stored in the case base.Ranking can be based on (property, property value) appearance # in the triples stored in the case base. System must recommend good starting points for user specification of servoObject class properties.System must recommend good starting points for user specification of servoObject class properties.

17. CCBR CASEBASE Case Feature 1 Feature 2 Feature 5 Feature 1 Feature 2 Feature 5 Case = Feature 1 Feature 2 Feature 3 Feature 4 Feature 1 Feature 2 Feature 3 Feature 4 A Case from CASEBASE Query Case IF ((A.Feature1.Solution = B.Feature1.Solution) & (A.Feature2.Solution = B.Feature2.Solution)) THEN Consistency # = 2 AB CCBR Recommender System

19. Recap: SERVOGrid Case Base Recommender System goal: locating resources in a large scale environment (SERVOGrid project) goal: locating resources in a large scale environment (SERVOGrid project) approach: approach: SERVOGrid ontology instances (metadata) to describe resourcesSERVOGrid ontology instances (metadata) to describe resources Recommender system to aid metadata discoveryRecommender system to aid metadata discovery Conversational CBR with SW markup languages providing standard form for case representationConversational CBR with SW markup languages providing standard form for case representation

20. More Information SERVOGrid/QuakeSim: SERVOGrid/QuakeSim: http://quakesim.jpl.nasa.gov/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. htmlhttp://tambora.ucs.indiana.edu/~maktas/servo/project. htmlhttp://tambora.ucs.indiana.edu/~maktas/servo/project. htmlhttp://tambora.ucs.indiana.edu/~maktas/servo/project. html SERVOGrid Recommender Systems demo: SERVOGrid Recommender Systems demo: http://ripvanwinkle.ucs.indiana.edu:4780/cbr/selection.j sphttp://ripvanwinkle.ucs.indiana.edu:4780/cbr/selection.j sp Publications: Publications: http://grids.ucs.indiana.edu/ptliupages/publications/http://grids.ucs.indiana.edu/ptliupages/publications/http://grids.ucs.indiana.edu/ptliupages/publications/

21. Questions/Comments Any questions and/or comments? Any questions and/or comments? Thanks! Thanks!