sharing our understanding of earth science resources

www.geongrid.orgCYBERINFRASTRUCTURE FOR THE GEOSCIENCES

Sharing Our Understanding Of Earth Science Resources

A knowledge management portal to support collaborative geoscience

Mark GaheganBill PikeSachin OswalGary SheppardGary LiuBrandi NagleJunyan Luo

GeoVISTA Center, Department of Geography, Pennsylvania State University


Introduction, motivation & year 2 goal

Making electronic geoscience resources more available is not enough… We need to be able to describe these resources

more effectively…

To be successful, contributing and finding resources must become an integral part of the way scientists/educators work

Major goal for year 2…Develop visually-based tools to help geoscientists organize, describe, and gain access to the GEON resources


instantiation

conceptualization

Knowledge management for collaborative geoscience

Representation

Capture

Usage

• top-down ontology languages• bottom-up context, situations (provenance)• visual appearance, signification• history & evolution • alternative descriptions

• collaborative web interface• diagramming tools• text mining tools• importing existing ontologies• workflow discovery

• ontology mediation services• ontology similarity measures• browsing conceptual structures • shared virtual workspace


Representation

Ontology languages (OWL, RDF, DAML+OIL) Association histories of how resources are used Visual appearance / signification serialization Additional descriptive information / resources

</owl:Class><owl:Class rdf:ID="Marsh"><rdfs:subClassOf rdf:resource="#CoastalRegion"/><rdfs:subClassOf rdf:resource="#WetlandRegion"/></owl:Class>… Fragment of OWL ontology from NASA’s EarthRealm project


Contextualizing science

- Richard Bernstein Beyond Objectivism and Relativism: Science, Hermeneutics, and Praxis

“In science, numerous lines of investigation interweave to delineate a type of rationality that is historically situated and practical, and involves choice, deliberation, and judgment.” Richard Bernstein Beyond Objectivism and Relativism: Science, Hermeneutics, and Praxis

Our aim is to contextualize resources through experiences; this is crucial for understanding in domains that are highly interpretivePut another way, what do feeding ducks have in common with 50% of our understanding?


Three problems with a solely ontological approach

Top down knowledge (ontologies) only get you so far… other kinds of (bottom up) knowledge are also very important & useful Use-cases (situations surrounding the use of resources) Social networks

Most current ontologies are static resources… Our understanding is dynamic & continually evolving Unless ontologies are community-owned, dynamic

resources they will soon become part of the problem, not part of the solution

What happens to all the thousands of resources that predate ontologies? The cost of retro-fitting ontologies is prohibitive.


Associations


Amazon Web Services, degrees of separation using the Amazing (Kevin) Baconizer (www.baconizer.com)

From "How Maps Work" by MacEachren, Alan to "Oops I Did It Again" by Spears, Britney: 12 hops –

People who bought: How Maps Work: Representation, Visualization, and Design - By Alan M. MacEachren

also bought: Web Cartography - By M-J Kraak and Allan BrownPeople who bought this also bought:Seeing Through Maps: The Power of Images to Shape Our World View - By Ward Kaiser and

Denis WoodMapping: An Illustrated Guide to Graphic Navigational Systems - By Robert Fawcett-TanWhat is a Designer: Things, Places, Messages - By N Potter and R KinrossReinventing the Wheel - By Jessica HelfandPhotobooth - By Babbette HinesMTV Photobooth - By MTV and Rizzoli International PublicationsStages - By Britney Spears and Sheryl BerkBritney Spears - By Britney SpearsBaby One More Time (+5 Bonus Tracks) - By Britney Spears

Oops I Did It Again - By Britney Spears


Capturing use-cases

Who created that concept / resource?

When was it created?Has it been modified recently?Who has used it? …What did they do with it?

Such questions add a rich context by capturing situations surrounding resource usage


Resource usage data

logged usage data (Oracle, MySQL)


Mining association rules from use-case logs

Association rules are mined from user action logs (uses the WEKA (Waikato Environment for Knowledge Analysis) API that implemented the Apriori algorithm (Agrawal, R. and Srikant, R., 1994).

Tools added for data preprocessing and classifying: attribute selector: allows user to select a subset of data

attributes. data filters: allows user to define filters to convert String,

Time, Numeric data in any attribute column to nominal data for association mining.


Design

Attribute Selector

Data Filter - Numeric

Data Filter - String

Data Filter - Time

Data mining tools (association rules)Results & sensitivity settings


Capture: concept creation & harvesting (Codex, e-Delphi)


Capture example (Randy Keller’s gravity map from previous GEON meeting)


Supplemental material: e.g. educational resources


Supplemental material: e.g. Google search results


Google search (Google search API is built into Codex)


Usage

codex demonstration


Managing groups & user workspaces


Reusable knowledge structures afford…

Private and shared knowledge spaces for describing resources

Provenance information produces a web of relationships between resources

Evolution and emergence of ideas within a community

Discovery of points of agreement and divergence in concept construction or problem-solving approaches

http://flatbox.geog.psu.edu/codex


Example: questions you can ask

Gravitational anomaly dataset A Is described by these concept map(s) / ontologies: Was created in this way: Plays a role in these workflow(s): Has been used to fulfill these task(s): Has been used by these people: Is most often used with these method(s) Has received the following review(s) / feedback: Is similar to, or differs from, anomaly dataset B in

the following way(s):


Future plans

Add more perspectives onto resources into Codex (e.g. working with Digital Library for Earth Science Education (DLESE))

Improve transition from one perspective to another

Peer-to-peer implementation Improve transition between semi-formal concept

maps (provided by domain scientists) and formal (computable) ontologies that are defined more rigorously.

Experiment with Codex used live to capture conceptual understanding (face to face and over the Web)


Summary: projects we are perusing for GEON

1. Concept map / ontology visualization & management tools (ConceptVista & Codex): searching & browsing of knowledge domains, and other resources.

2. Concept capture software (e-Delphi, Codex): developing vocabularies by which resources and learning activities are described

3. Concept map / ontology versioning and comparison (differencing)4. Concept uncertainty (fuzzy-rough set approach)5. Use-Case Tools: logging and data mining (association rules)6. Visualization and analysis tools: e.g. animated maps, scatterplots,

3D scenes, cluster analysis, machine learning methods7. Component assembly and deployment (GeoVISTA Studio): could

help in selecting and packaging activities into self-contained, deployable units.

8. Managing learning activities: Learning Activity Toolkit (Southampton, UK & PSU)

9. Integration of concept management with DLESE API & strand maps


Publications1. Pike W., Gahegan M, 2003, “Constructing semantically scalable cognitive spaces”, in:

Spatial Information Theory: Foundations of Geographic Information Science. Lecture Notes in Computer Science 2825, Kuhn W, Worboys M, and Timpf S (Eds.). Springer-Verlag, Berlin: 332-348.

2. MacEachren A M, Gahegan M, Pike W, 2004, “Geovisualization for constructing and sharing concepts”, Proceedings of the National Academy of Sciences, Vol. 101.

3. Gahegan M, Pike W, Ahlqvist O, Neff R, Yu C, “How much do we agree? A knowledge management system to help represent and mediate concepts developed by collaborating human-environment researchers” submitted to Annals of the Association of American Geographers.

4. Gahegan, (2004). “Beyond tools: visual support for the entire process of GIScience. “ In: Exploring Visualization (Eds. Dykes, J., MacEachren, M. and Kraak, J.-M.)

5. Brodaric, B. and Gahegan, M. (in press) “Representing Geoscientific Knowledge in Cyberinfrastructure: challenges, approaches and implementations”. GSA Special Papers volume.

6. O’Brien, J. and Gahegan, M. (2004). “A knowledge framework for representing, manipulating and reasoning with geographic semantics.” International Conference on Spatial Data Handling, Leicester.

7. Gahegan, M. (2004). “The Future of GIScience? GRID Computing and the Semantic Web”. Keynote address, GISRUK Conference, www.gisruk.org

8. Pike W, Yarnal B, MacEachren A, Gahegan M, Yu C, (in press) “Infrastructure for collaboration: Building the future for local environmental change”, to appear in Environment.

9. Pike W. A., Ahlqvist O., Gahegan M., Oswal S., “Capturing context in collaborative science: Supporting collaborative science through a knowledge and data management portal,” Workshop on Semantic Web Technologies for Searching and Retrieving Scientific Data, at Second International Semantic Web Conference, Sanibel Island, FL, October 2003.


end

Questions?


Supplemental slides


Managing and sharing visual appearance

ConceptHierarchies

Concept Graph

Style Editor

A Hierarchical View of the Concepts

Concepts are listed Alphabetically

Currently We Support RDF, OWL, and XML.

Concepts are Represented as Nodes, and their relations are represented as Edges.

Styles describe how concepts should be rendered.

Different concepts can have different styles using property filters

Styles can be serialized using XML-based Styled-Layered Descriptor Language, (SLD)


Cyber-Infrastructure: underlying technologies

Peer-to-Peer (P2P) Computing, software technology that enables networked computers to communicate (exchange information) without a common operating environment. The Information Power Grid (IPG) and Globus provide protocols

Web Services, provide standards to describe, find & access remote resources. Web services mechanisms are integrated into the Grid model

through the Open Grid Services Architecture (OGSA). Semantic Web, describing and searching for web content

using formalized semantics (controlled vocabularies, taxonomies, ontologies) … as opposed to the current ‘chaos’, largely based on literals,

popularity & corporate sponsorship! Collaborative Knowledge Environments,

Data & Knowledge portals Asynchronous discussions Video conferencing


Towards a knowledge collaboratory


Knowledge Infrastructure constructing &

accessing knowledge

Knowledge Infrastructure constructing &

accessing knowledge

Group Work with geospatial information & technologies

Group Work with geospatial information & technologies

Supporting knowledge evolution

Supporting work

practices

Visually mediating

understanding

Off-loading ideas

Geospatial data repositories

Automated indexing

tools

Ontology and concept

browsers

Existing metadata standards

An Integrated Approach to Distributed GeoCollaboration

Semantic web

Making decisions

Enabling negotiation

Meta-search (ensemble techniques)

Collaboratories

Content-object replication kit (CORK)

e-Delphi, ConceptVISTA, & argument visualization

Dialogue-enabled interface

Collaborative visualization

K-12 science & professional development

Public/civic planning/resource

management

Geo / Environmental

science

Emergency response &

recovery

NGA: NSGI

Geospatial One-Stop

Digital Earth

National MapGEON HERO

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leng

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infrastructure for geoscience infrastructure for homeland securityinfrastructure for e-government/e-society

advancing scienceenhancing prosperity & civil society

supporting homeland security

acquiringknowledge

integratingknowledge applying

knowledge

N e w K own ledge

EP

f f ece

vt i ic ctr ae

Supporting knowledge

communities

Representing and sharing perspectives

Distributing access to knowledge

Browsing & querying

knowledge

Semantic search

Semantic indexing

Ontology mining /

harvesting

Ontology creation

Strategic threat

assessment


Contexts:

Creation Application Represented by

Who did it? Who should use it? Collections of people

Where was it made? Where does it apply?

Collections of sites / scales

When was it made? When does it apply? Collections of temporal intervals

How was it made? How should it be used?

Collections of methods and data

Why was it made? Why should it be used?

Collections of research questions, motivations, theories

sharing our understanding of earth science resources

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