connecting and synchronizing scientific knowledge

Prashant Gupta, Mark Gahegan and Gill Dobbie

The University of Auckland New Zealand

ConnecBng and synchronizing scienBfic knowledge

The current state of scienBfic pracBces

The current state of scienBfic pracBces

How well are we carrying forward the core principles of science (communicaBon, repeatability and refutability) with the new scienBfic pracBces?

Learning from the past


Map

Categories


Map

Categories

How do we connect them back to synthesize an integrated view ?

Knowledge producer and consumer perspecBve

Data Methods

Analysis Map

Workflow

Data model Knowledge producer

Knowledge producer and consumer perspecBve

Data Methods

Analysis Map

Workflow

Data model Knowledge producer

It’s very confusing. They are all disconnected. Hard to say how they were used. I wish they had some

explicit connecBons.

Knowledge consumer

FragmentaBon of scienBfic arBfacts and processes among communiBes

Community 2

Image processing tools

CSV/XML/database

Image processing

DigiBzed data

Community 1 Remote sensing

system

Data observaBon

Satellite Imagery

Concepts

Database

Community 4

ClassificaBon

Machine learning tool Land-‐cover dataset

Web-‐mapping tool

Taxonomy tool

Community 5

Land-‐cover map

Taxonomy

ApplicaBons

Community 3

Field work Aerial photography

Training data

CSV/XML/database

FragmentaBon of scienBfic arBfacts and processes among communiBes

Richer/beXer data

Community 2

Image processing tools

CSV/XML/database

Image processing

DigiBzed data

New validaBon techniques

Conceptual change

New ideas

Algorithmic improvement

Community 1 Remote sensing

system

Data observaBon

Satellite Imagery

Concepts

Database

Community 4

ClassificaBon

Machine learning tool Land-‐cover dataset

Web-‐mapping tool

Taxonomy tool

Community 5

Land-‐cover map

Taxonomy

ApplicaBons

Community 3

Field work Aerial photography

Training data

CSV/XML/database

New/beXer technology

State of knowledge integraBon

Data

Models

Articles

External Databases

http://www.seek4science.org

Metadata

http://www.isatools.org

Interlinking methods, models, data, samples..

Other knowledge integraBon models..

•  Research Objects S. Bechhofer, D. De Roure, M. Gamble, C. Goble, and I. Buchan, “Research objects: Towards exchange and reuse of digital knowledge,” presented at The Future of the Web for CollaboraBve Science, NC, USA, 2010.

•  Reproducible Research System J. P. Mesirov, “Accessible reproducible research,” Science, Jan. 2010.

•  Linked Science T. Kauppinen and G. M. Espindola, “Linked open science communicaBng, sharing and evaluaBng data, methods and results for executable papers,” presented at the Int. Conf. ComputaBonal Science (ICCS), 2011.

•  Workflows

Other knowledge integraBon models..

•  Research Objects S. Bechhofer, D. De Roure, M. Gamble, C. Goble, and I. Buchan, “Research objects: Towards exchange and reuse of digital knowledge,” presented at The Future of the Web for CollaboraBve Science, NC, USA, 2010.

•  Reproducible Research System J. P. Mesirov, “Accessible reproducible research,” Science, Jan. 2010.

•  Linked Science T. Kauppinen and G. M. Espindola, “Linked open science communicaBng, sharing and evaluaBng data, methods and results for executable papers,” presented at the Int. Conf. ComputaBonal Science (ICCS), 2011.

•  Workflows

What are the shortcomings?

•  Focus on a single experiment of science, rather than science as an ongoing and evolving process

•  Provide a linear view of science, but science is instead exploratory, dynamic and cyclic

•  Focus typically on data and not on conceptual structures

A model that supports living and linked scienBfic knowledge

AssociaBonist view

A model that supports living and linked scienBfic knowledge

AssociaBonist view

Organic view – born, evolve and

die

ConnecBng scienBfic arBfacts

Data Database schema

Sogware tools

Categories Map Ontology

Live connecBons among scienBfic

arBfacts

Includes e-‐Science tools and process models

Example

Data Database schema

Sogware tools

Categories Map Ontology

1.  If a new classifier method is used for land cover classificaBon, it may lead changes to categories

2.  The extension of the category ‘Forest’ changes, leading to change in the data stored under that category.

3.  Finally, the change in data is reflected in the land cover map

1

2

3

Adventures of Categories (AdvoCate)

•  An e-‐Science tool that incorporates the process model of category evoluBon

•  The system allows researchers to model changes in categories, captures the process of evoluBon and maintains a category-‐versioning system

•  Connect changes in categories with the tools supporBng database and ontology evoluBon

Process model of category evoluBon

External change drivers

Revising categorical model

EvaluaBon of categorical model

Change approval

Change report (using elementary and complex

change operaBons)

ImplemenBng the changes & updaBng category versioning

system

Change PropagaBon





Change approval


change operaBons)


system

Change PropagaBon

•  New observaBon (training data)

•  Societal drivers •  New understanding

•  New category •  Splikng or merging of

categories •  Drig in categories

•  Elementary changes: •  Add/Delete category •  Add/Delete relaBonship •  Change label •  Change intension

•  Composite changes: •  Born •  Die •  Merge •  Split •  Drig





Change approval


change operaBons)


system

Change PropagaBon

Ontology/database evoluBon tools

Process of science

Data model

An example of category evoluBon from land cover mapping

Future work

•  Formalize data model using semanBc technologies

•  Change recogniBon rules for various categories models (probability distribuBon models, rule-‐based models, etc.)

•  VisualizaBon of categories evoluBon •  Change broadcasBng service to ontology and database evoluBon tools

QuesBons ??