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