Difference between revisions of "Community concept mapping activity March 2020 through July 2020"

From Earth Science Information Partners (ESIP)
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'''Why we're doing this''':   
 
'''Why we're doing this''':   
* '''Context awareness and knowing where you fit in the big picture.'''  So that professionals like yourself are able to start at an applied challenge and work back towards how technologies of interest to the ESIP community can be integrated to solve that challenge.
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* '''Context awareness and knowing where you fit in the big picture.'''  So that professionals like yourself are able to see how technology you develop may be traced to a real-world applied problem, or vice versa.
 
* '''Context-aware knowledge discovery.'''  So that the community can start experimenting with using natural language processing methods to parse human-readable concept maps.  Such maps are not as machine-readable as, say, an ontology crafted in OWL.  However, such maps are an effective way to communicate with stakeholders, and can also be repurposed to inform machine-assisted discovery of data and information relevant to a real-world challenge.  Queries with boolean operators, coupled with semantic technology, is great, but wouldn't it be even better if knowledge discovery algorithms reflect the inference and association patterns exhibited by human thinking that allows us to reason in the abstract?
 
* '''Context-aware knowledge discovery.'''  So that the community can start experimenting with using natural language processing methods to parse human-readable concept maps.  Such maps are not as machine-readable as, say, an ontology crafted in OWL.  However, such maps are an effective way to communicate with stakeholders, and can also be repurposed to inform machine-assisted discovery of data and information relevant to a real-world challenge.  Queries with boolean operators, coupled with semantic technology, is great, but wouldn't it be even better if knowledge discovery algorithms reflect the inference and association patterns exhibited by human thinking that allows us to reason in the abstract?
 
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Revision as of 09:05, March 26, 2020

Objective: Between March 2020 and July 2020, we aim to create a small collection of concept maps that ESIP participants are encouraged to contribute to. This collection of concept maps will focus on a hypothetical use-case for a high-level challenge. In this case, the use-case focuses on a climate adaptation challenge for poultry agriculture in the Chesapeake watershed (below). Selected concepts within the collection of concept maps (the concept map "soup") will lead to other concept maps that describe knowledge tools (e.g. web applications, searchable online repositories), data products, and the underlying technologies (e.g. data services, machine learning techniques). The "path" taken from higher-level concepts to technology components represents a notional data-to-decisions pathway.

What we need from you: No individual has the sufficient knowledge to reproduce a comprehensive data-to-decisions pathway. We have sketched out a notional pathway, and hope that you can contribute filling in that pathway by contributing your expertise at various spots along the pathway. The figure below gives you a rough idea of what we mean, and how you can plug-in your own concept map:

2020-03-20.Concept map icons for the data-to-decisions demo path wiki page.jpg

Why we're doing this:

  • Context awareness and knowing where you fit in the big picture. So that professionals like yourself are able to see how technology you develop may be traced to a real-world applied problem, or vice versa.
  • Context-aware knowledge discovery. So that the community can start experimenting with using natural language processing methods to parse human-readable concept maps. Such maps are not as machine-readable as, say, an ontology crafted in OWL. However, such maps are an effective way to communicate with stakeholders, and can also be repurposed to inform machine-assisted discovery of data and information relevant to a real-world challenge. Queries with boolean operators, coupled with semantic technology, is great, but wouldn't it be even better if knowledge discovery algorithms reflect the inference and association patterns exhibited by human thinking that allows us to reason in the abstract?


Scenario:
The concept map below represents a hypothetical sketch of the subset of climate adaptation challenges for the Chesapeake watershed. A inter-county task force ("task force") assembled various stakeholders (e.g. business owners, critical government infrastructure operators, economic planners, scientists, environmental engineers, etc) and created a concept map to communicate the stakeholder's collective perception of the challenge. Further to that, the task force has charged a small team to develop a technology plan that would support decision-making within the scope of issues outlined the concept map. The poultry agriculture industry is faced with pressing concerns, and the team decides to start developing the technology plan focused on the poultry industry, but mindful that the technology plan needs to expand to other concerns later.

To browse the hypothetical use case below (to contribute and edit the map, see below, after the map):
Click only on the orange-colored nodes as you navigate the concept map soup. These orange colored nodes contain a pathway that includes technology components that have been partially filled in. We are seeking concept map contributions from you for concepts along this partially complete pathway.

For the technically inclined: If you see a node that ends with the expression ~sem.ann, that node contains a semantic annotation. Mouse-over the node to see the annotation. Annotations are based on OBO Foundry ontologies like ENVO (Environment Ontology), AGRO (Agronomy Ontology), and SDGIO (Sustainable Development Goals Interface Ontology). Please excuse the inconsistency in node annotation, because this is a phase of active experimentation. The experiment may be described as: "will semantic annotations of human-readable concepts (and perhaps predicates that connect concepts) enable these concept maps to be used for machine-assisted discovery of pertinent digital research objects?"



To contribute and edit the map <instructions coming!>