Leonardo Salayandia

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Workflow-Driven Ontologies for the Geosciences: 

Workflow-Driven Ontologies for the Geosciences Leonardo Salayandía The University of Texas at El Paso

Overview: 

Overview Background Cyberinfrastructure Ontologies Workflows Purpose of this talk The Workflow-Driven Ontology approach Knowledge capture Workflow creation from WDOs Benefits of WDOs Status Summary

Cyberinfrastructure: 

Cyberinfrastructure S-wave tomography models GPS plate motion vectors Global Strain Rate Map GEON IDV (Integrated Data Viewer) [http://geon.unavco.org]

Cyberinfrastructure: 

Cyberinfrastructure S-wave tomography models GPS plate motion vectors Global Strain Rate Map GEON IDV Distributed sources of information Information in different formats Distributed tools and applications

Cyberinfrastructure: 

Cyberinfrastructure People and resources connected through the web Enhanced collaboration over distance, time, and disciplines Interoperate across institutions and disciplines Preserve and maintain availability of software and data

Cyberinfrastructure: 

Cyberinfrastructure People and resources connected through the web Enhanced collaboration over distance, time, and disciplines Interoperate across institutions and disciplines Preserve and maintain availability of software and data

Ontologies: 

Ontologies A specification of a conceptualization Concepts (or classes of objects) Concept1: S-wave tomography model (TM) Concept2: Geospatial representation Relationships between concepts S-wave TM HAS Geospatial Representation

Workflows: 

Workflows Recipes for accomplishing some complex task Composition of service modules (CI services) Automate tedious and time-consuming tasks Useful for experiment replication Example:

Workflows: 

Workflows Recipes for accomplishing some complex task Composition of service modules (CI services) Automate tedious and time-consuming tasks Useful for experiment recreation Example: S-wave tomography data Create Model S-wave tomography model Service to get the data Service to transform data Transformed data outcome

Cyberinfrastructure: 

Cyberinfrastructure [B. Ludäescher, 2006]

Cyberinfrastructure: 

Cyberinfrastructure [B. Ludäescher, 2006] Workflows Ontologies

Purpose of talk: 

Purpose of talk Show an approach for scientists to capture knowledge in a way that can be leveraged towards CI Create ontology specifications Generate workflows from ontologies

Purpose of talk: 

Purpose of talk Show an approach for scientists to capture knowledge in a way that can be leveraged towards CI Create ontology specifications Generate workflows from ontologies Workflow-Driven Ontologies (WDOs)

Example: Gravity WDO: 

Example: Gravity WDO Geoscientist I use geophysical data to elucidate the tectonic development of the North American craton I want to produce a gravity data contour map. These are the steps that I go through to do it: Contour Map Grid Gravity Data Get the data Create a grid of uniformly distributed points from this data Use the grid as input to render the map Dr. Randy Keller

Capture Knowledge: 

Capture Knowledge Contour Map Grid Gravity Data Different types of Information

Capture Knowledge: 

Capture Knowledge Contour Map Grid Gravity Data Information Raw Data Processed Data Product How is the information transformed? Is converted to Is rendered into

Capture Knowledge: 

Capture Knowledge Contour Map Grid Gravity Data Information Raw Data Processed Data Product Contouring Algorithm Gridding Algorithm Methods Is input into Is input into Outputs Outputs Is converted to Is rendered into

Class Hierarchy for WDOs: 

Class Hierarchy for WDOs Root Information Methods Data Product Raw Data Processed Data Gravity Data Grid Contour Map Gridding Contouring Common classes for all WDOs Classes specific to the Gravity WDO

Workflow specification generated from Gravity WDO: 

Workflow specification generated from Gravity WDO Root Information Methods Data Product Raw Data Processed Data Gravity Data Grid Gridding Is input into Outputs CI Service1: Gravity Data Extraction CI Service2: Gridding Result Mapping between WDO classes and CI services

From workflow specification to workflow implementation: 

From workflow specification to workflow implementation Workflow engines: Kepler scientific workflows (GEON et al.) OWL-S (Semantic Web) Many others… Workflow specifications produced from WDOs can potentially be “realized” in any service-oriented workflow engine

Benefits of WDOs: 

Benefits of WDOs Scientific products drive the creation of the WDO Incremental development WDO serves as roadmap for future CI service development Identify missing services for potentially useful workflows Generated workflows serve as a gauge for the usefulness of an ontology

Status: 

Status Gravity WDO prototype Workflows in the process of being implemented in the Kepler Scientific Workflow Engine WDO Assistant and API software

The Gravity WDO: 

The Gravity WDO First WDO prototype (Flor Salcedo, Randy Keller, and Ann Gates)

Status: 

Status Gravity WDO prototype Workflows in the process of being implemented in the Kepler Scientific Workflow Engine WDO Assistant and API software

WDO Assistant and API: 

WDO Assistant and API Prototype built on top of the Jena API Java programming language Three modes of operation Brainstorming Elicitation Workflow Generation

WDO Assistant and API: 

WDO Assistant and API Brainstorming mode Scientists define concepts that relate to CI information and methods

WDO Assistant and API: 

WDO Assistant and API Elicitation mode Scientists define relationships between concepts

WDO Assistant and API: 

WDO Assistant and API Workflow Generation mode Scientists choose information concept for which to generate a workflow, as well as target workflow engine

Future Work: 

Future Work CI-Miner Provenance information Trust information Preferences

Slide30: 

OWL onts. Generic CI Portal WDOs Composite OWL-S Service WFGen Atomic OWL-S Service PSW A Service Answer/ provenance visualization CI-Trust CI Miner PML TrustNet CI-Base (IWBase) Service execution OWL-S API CI-Browser ontologies calls uses Legend creates Trust Recommendation CI-Browser WDO API JENA CI Background Tools WDO Assistant Knowledge capture Protégé, SWOOP

Summary: 

Summary In order to realize the goals of CI there is a need to Capture domain knowledge Use the domain knowledge to “glue” resources together The WDO approach Allows scientists (not computer programmers) to incrementally capture knowledge as needed Facilitates communication between scientists and computer programmers to produce CI resources that “stick” to other resources

Thank you: 

Thank you

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