Knowledge Networks and Science Data Ecosystems December 7, 2012, AGU12 IN54A-02. Peter Fox (RPI/ Tetherless World Constellation and WHOI/AOP&E)

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Presentation transcript:

Knowledge Networks and Science Data Ecosystems December 7, 2012, AGU12 IN54A-02. Peter Fox (RPI/ Tetherless World Constellation and WHOI/AOP&E)

What’s ahead/pre-summary Data ecosystems necessarily involve a variety of stakeholders Complex relations require us to move beyond ‘simple’ networks To do that, we: –Define the framework for a knowledge base, and populate it –Query and render the result 2Tetherless World Constellation

3 DataInformationKnowledge Context Presentation Organization Integration Conversation Creation Gathering Experience Ecosystem

You mean, you want to know?

Rendering of a network logd.tw.rpi.edu/demos

Marine ecosystems

Science ecosystem Figure Acknowledgement: Suzanne Lawrence

Complex networks

‘Complex’ networks Based on information content –Node count and number of links –Spanning height –‘Width’ –Etc. Or complexity metrics, cf. McCabe – number of linearly independent paths through the network Open world networks lead to differing node relations… (or not)

Complex :== Meaning in context Semantic networks are ones where the nodes and relations are ‘named and typed’

What about meaning Named and typed relationships Name2:type2 Name1:type1 Name2:type2 Namen:typen

Framework - DPSIR

3/9/1214 Water Quantity

Models for ecosystems

Instances

Network Scale(s) Complex :== Meaning Semantic networks are ones where the nodes and relations are ‘named and typed’ Interesting property - scale-free –Citation networks –The Web –Semantic networks –Depend on super nodes

Scale free? More likely – multi-scale with some hierarchy …

Vision being implemented “Our vision is to develop, facilitate, and maintain sustained multi-way engagement of natural and social scientists and many practitioners in multi- scale local to global networks for Large Marine Ecosystems (LMEs)”. Goal: Perform routine assessments of LMEs involving all (or as many) stakeholders and we want robust science data presented in forms that various end-users can consume…

Discussion We have the tools to explore these networks, collaboratively… Now looking at network rendering, i.e. queries and visualizations Current limit is: base information models upon which to develop the initial knowledge base(s) (i.e. ontologies) Next: Compute deductive closure, iterate… ? Thanks for listening. Questions?