as we may link: a model to support aggregated scientific knowledge
DESCRIPTION
Today, researchers are bogged down by continually growing amount of complex and diverse scientific knowledge, fragmented and dispersed among various disciplines, communities and information resources. Contemporary digital tools are efficient in dealing with complexity and diversity of scientific knowledge and the process of science, but they have compartmentalized scientific knowledge among various disparate and disconnected systems. For example, databases are used to structure data to facilitate its easy retrieval; workflows are used to represent the process of experiments; analytical tools are used to support analyzing data and visualization tools to visualize data and results to gain better understanding. However, they rarely connect or join together to synthesize an integrated view. Our digital knowledge ecosystem is siloed and poses a challenge for researchers to search, comprehend and reproduce scientific experiments. Vannevar Bush, in his article ‘As we may think’, discussed the huge data and information deluge and the challenge brought by the fragmentary nature of scientific knowledge. He proposed an imaginary machine – Memex – that could tie knowledge records in a mesh of associative trails, which can be reviewed and consulted as a form of graph search. This talk will discuss a model that adopts Bush’s associationist view to integrate scientific knowledge. Categories are commonly used in databases (in the form of logical schema) and ontologies (as concepts and properties), but often these artifacts are disconnected from eachother. The proposed model connects categories, along with their process of construction and evolution, with a database and ontology via tools that support their evolution. Connecting these knowledge artifacts (via their digital tools) explicitly not only provides an integrated view, but may also be capitalized to support mediation among these artifacts and keeping them consistent with new conceptualization. Such mediation among scientific artifacts will reconnect the computationally enabled science and the knowledge underpinning it.TRANSCRIPT
As We May Link A model to support aggregated scien7fic knowledge
Centre for eResearch Dept. of Computer Science University of Auckland
Prashant Gupta (PhD student) Prof. Mark Gahegan Prof. Gillian Dobbie
The current state of scien7fic prac7ces
The current state of scien7fic prac7ces
How well are we carrying forward the core principles of science (reproducibility,
communica7on, etc. ) with these new prac7ces?
Learning from the past
Map
Categories
Learning from the past
Map
Categories
How do we connect them back to synthesize an integrated view ?
Learning from the past
“As we may think” (The Atlan*c, 1945)
hSp://thesciencebookstore.com/2009/10/the-‐history-‐of-‐the-‐internet-‐remembering-‐vannevar-‐bush-‐and-‐the-‐memex-‐1945/
Making conceptual connec7ons explicit
Map
Associa7onist view
A model that propose connected science
A model that propose connected science
Associa7onist view
A model that propose connected science ^
Live and
Associa7onist view
Organic view – born, evolve and
die
Connec7ng scien7fic ar7facts
Data Database schema
So]ware tools
Categories Map Ontology
Connec7ng scien7fic ar7facts
Data Database schema
So]ware tools
Categories Map Ontology
Connec7ng scien7fic ar7facts
Data Database schema
So]ware tools
Categories Map Ontology
Live connec7ons among scien7fic
ar7facts
Includes e-‐Science tools and process models
Example
Data Database schema
So]ware tools
Categories Map Ontology
Example
Data Database schema
So]ware tools
Categories Map Ontology
1. The k-‐means classifier used for land cover classifica7on changes, which lead to change in the categorical model
1
Example
Data Database schema
So]ware tools
Categories Map Ontology
1. The k-‐means classifier used for land cover classifica7on changes, which lead to change in the categorical model
2. The extension of category ‘Forest’ changes leading to change in the data stored under the category.
1
2
Example
Data Database schema
So]ware tools
Categories Map Ontology
1. The k-‐means classifier used for land cover classifica7on changes, which lead to change in the categorical model
2. The extension of category ‘Forest’ changes leading to change in the data stored under the category.
3. Finally, the change in data is reflected in the land cover map
1
2
3
Adventure of Categories (AdvoCate)
• Allows to model changes in categories • Maintains a category-‐versioning system • Connects data, methods and categories along with the different versions of them
• Connect changes in categories with the tools suppor7ng database and ontology evolu7on tools
Conclusion
• E-‐Science tools not only enable scien7fic ac7vi7es, but also provides an opportunity to bridge the gap between science and technology and ground our scien7fic tools in the process of science.
• This model supports live and connected science, which will surface up more scien7fic processes and deeper understanding in our computa7onally enabled science.
Ques7ons?
Prashant Gupta [email protected]
@pgupta_nz
Special thanks to Google for sponsored 7ckets to the conference J