kefed introduction 12-05-10-2224
DESCRIPTION
PDF Version of the introduction to the KEfED formalism.TRANSCRIPT
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Knowledge Engineering from Experimental Design
‘KEfED’ Gully APC Burns
Information Sciences Institute University of Southern California
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The Cycle of Scientific Investigation (‘CoSI’)
Knowledge Engineering from Experimental Design
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A typical seminar slide
What is an elemental piece of biomedical scientific knowledge?
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For example...
What is an elemental piece of biomedical scientific knowledge?
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The challenge of defining the biomedical semantic web
• Currently consists of a very large number of statements like ‘mice like cheese’ – semantics at this level are complicated!
• For example: – “Novel neurotrophic factor CDNF protects midbrain dopamine neurons
in vivo” [Lindholm et al 2007] – “Hippocampo-hypothalamic connections: origin in subicular cortex,
not ammon's horn.” [Swanson & Cowan 1975] – “Intravenous 2-deoxy-D-glucose injection rapidly elevates levels of the
phosphorylated forms of p44/42 mitogen-activated protein kinases (extracellularly regulated kinases 1/2) in rat hypothalamic parvicellular paraventricular neurons.” [Khan & Watts 2004]
• Statements vary in their levels of reliability, specificity. • Existing semantic web approaches involve representations of
argumentation / claim networks • Can we invent a new way to introduce formalism?
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Knowledge Engineering from Experimental Design (‘KEfED’)
• There is an implicit reasoning model employed by scientists to represent their observations based on the way they design experiments – Standardized experimental templates
– Parameters [‘Independent Variables’] – Measurements [‘Dependent Variables’] – Calculations [‘Derived Variables’]
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Basic KEfED Elements
Logical Element Icon Activity
Experimental Object
Parameter
Measurement
Branch
Fork
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Dependencies between variables are inherent in the experimental protocol
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The KEfED Model is intuitive
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KEfED handles complex experimental designs
More Below…
Khan et al. (2007), J. Neurosci. 27:7344-60 [expt 2]
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KEfED handles complex designs
Khan et al. (2007), J. Neurosci. 27:7344-60 [expt 2]
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Example : Neural Connectivity - Observations
‘anterograde’
‘retrograde’
Tract Tracing Experiments Neuroanatomical experiments to study neural connectivity.
injection-site
tracer-chemical
labeling-location
labeling-density
labeling-type
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Example : Neural Connectivity - Interpretations
Tract Tracing Experiments > Neuroanatomical Elements Interpretative entities that correspond to facts that may be aggregated into a model
Neuronal Population
cell-bodies
cell-bodies.location
terminal-field.location
terminal-field
‘Neural Connection’
connection-origin
connection-termination
connection-strength
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1st look at ‘BioScholar system’: Neural Connectivity Reasoning Tool
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Peeking Under the Hood
‘PHAL Injection into SUBv generates labeling in MM’ => ‘SUBv contains neurons that project to MM’ (expressed in First-Order-Logic within Powerloom Reasoner)
Computation based on the context of each measurement based on parameters
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Crux
• KEfED as the basis for the design of a data repository
• Collaboration with MSU + Science Commons – Funded by MJFF + Kinetics Foundation to
manage data from grantees
• KEfED-editor can as a component in an external web-application
[http://yogo.msu.montana.edu/applications/crux.html]
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Using Semantic Web Standards
[https://wiki.birncommunity.org:8443/display/NEWBIRNCC/KEfED+OWL+Model]
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OBI
• Use a simplified ‘projection’ with no semantic entailments.
• Seek a simple model with semantics embedded ‘within’ variables
… work in progress here … • Seek semantic-web-based
links to: – OBI – SWAN / SIOC – ISA-Tab tools
• Domain-specific Reasoning Models (from ‘CoSI’)
– Want to generate hypotheses / predictions that can be expressed as KEfED models?
– $6,000,000 question!
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Future Directions
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Acknowledgements
Funding – Information Sciences Institute,
seed funding – NIGMS (R01GM083871) – NIMH (R01MH079068) – NSF (#0849977) – Michael J Fox + Kinetics
Foundations – BIRN @ ISI
Neuroscience Team Members – Rick Thompson (USC) – Jessica Turner (MRN)
Neuroscience Contributors – Alan Watts (USC) – Larry Swanson (USC) – Arshad Khan (USC)
Computer Scientist Team – Tom Russ (ISI) – Cartic Ramakrishnan (ISI) – Marcelo Tallis (ISI) – Eduard Hovy (ISI)
Other Team members – Alan Ruttenberg (ScienceCommons) – Michael Rogan (NYU) – Gwen Jacobs (MSU) – Pol Llovet (MSU)
Computer Scientist Contributors – Hans Chalupsky (ISI) – Jerry Hobbs (ISI) – Yolanda Gil (ISI) – Carl Kesselman (ISI) – Jose Luis Ambite (ISI)