workflow-driven ontologies for the geosciences

GEON Cyberinfrastructure WorkshopBeijing, China, July 21-23, 2006

Workflow-Driven Ontologies for the Geosciences

Leonardo Salayandía

The University of Texas at El Paso


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


CyberinfrastructureS-wave tomography models

GPS plate motion vectors

Global Strain Rate Map

GEON IDV(Integrated Data Viewer)

[http://geon.unavco.org]


CyberinfrastructureS-wave tomography models

GPS plate motion vectors

Global Strain Rate Map

GEON IDV

Distributed sources of informationInformation in different formats

Distributed tools and applications


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

• 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

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


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 ModelS-wave

tomography model

Service to get the data

Service to transform data

Transformed data outcome


Cyberinfrastructure

[B. Ludäescher, 2006]


Cyberinfrastructure

[B. Ludäescher, 2006]

Workflows

Ontologies


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

• 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

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

Contour Map

Grid

Gravity Data

Different types of Information


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

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 WDOsRoot

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

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

• 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

• 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

• Gravity WDO prototype– Workflows in the process of being

implemented in the Kepler Scientific Workflow Engine

• WDO Assistant and API software


The Gravity WDO• First WDO prototype (Flor Salcedo, Randy Keller,

and Ann Gates)


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

• Prototype built on top of the Jena API – Java programming language

• Three modes of operation– Brainstorming– Elicitation– Workflow Generation



• Brainstorming mode– Scientists define concepts that relate to CI

information and methods



• Elicitation mode– Scientists define relationships between

concepts



• Workflow Generation mode– Scientists choose information concept for

which to generate a workflow, as well as target workflow engine


Future Work

• CI-Miner– Provenance information– Trust information– Preferences


OWL onts.

GenericCI Portal

WDOs

CompositeOWL-SService

WFGen

AtomicOWL-S Service

PSW

A Service

Answer/provenancevisualization

CI-Trust

CI Miner

PML

TrustNet

CI-Base(IWBase)

Serviceexecution

OWL-SAPI

CI-Browser

ontologiescalls

uses

Legend

creates

TrustRecommendation

CI-Browser

WDO API

JENA

CI BackgroundTools

WDOAssistant

Knowledgecapture

Protégé, SWOOP


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

workflow-driven ontologies for the geosciences

Documents

conceptsswave tm

geophysical data

contour mapgridgravity

gravity data contour

timeconsuming tasks

availability of software

webenhanced collaboration

experiment recreationexample