Knowledge Engineering Start with the question: “What is an ‘atom’ of scientific knowledge?”
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Transcript of Knowledge Engineering Start with the question: “What is an ‘atom’ of scientific knowledge?”
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Knowledge Engineering
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Scientific assertions as ‘Computable, citable
elements’• There are 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]
• Assertions vary in their levels of reliability, specificity.• Can we introduce a generalized formalism that could
support automated reasoning?
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Cycles of Scientific Investigation (‘CoSI’)
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e.g., ‘CDNF protects nigral dopaminergic neurons in-vivo’
This statistically-significant effect is the experimental basis for the findings of this study.
Our ontology engineering approach is based on experimental variables
from Lindholm, P. et al. (2007), Nature, 448(7149): p. 73-7
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Knowledge Engineering from
Experimental Design (‘KEfED’)
Khan et al. (2007), J. Neurosci. 27:7344-60 [expt 2]
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KNOWLEDGE ENGINEERING FROM EXPERIMENTAL DESIGN‘KEfED’Project Overview
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Project History• The KEfED formalism has been under formulation since 2006 and
received it’s first active funding in 2007. It has been initially developed in a demonstration project based on neural connectivity and has been developed for the Michael J Fox and Kinetics Foundations for Parkinson’s research.
• The initial user group consists of laboratory-based neuroanatomists and neuroendocrinologists.
• Early phases of the project involved development of initial prototypes to capture the design of a well-understood experimental design and to generate a knowledge base for experimental data from that design.
• We have developed numerous prototypes but have deployed a working
system from the http://www.bioscholar.org website in March, 2011.
• Ongoing enhancements to the system include (a) ontology support, (b) the representation of statistical relations and correlations, (c) coordination with the data management and information integration working groups.
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BioScholarApplication
• Develop a knowledge base framework for observations and interpretations from experiments.
• Scientists manually curate data by hand from publications into generic database driven by KEfED model
• Can reuse designs for multiple experiments
• Design process is intuitive, can build a database without informatics training• Ideal for non-computational biologists.• Java / Flex Web application, one click
install
Use Cases
Scientists want to develop a generic knowledge base driven from a corpus of PDF files stored locally within a specific laboratory
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CruxApplication
• Scientists within a disease foundation must plot a whole research program
• How to keep track of hypotheses, experimental results and outcomes to plan the next phase of the project?
• System is just about to start year 2 of funding geared towards curation of raw data (not from publications).
• Possible framework to help scientists develop simple databases.
Use Cases
Decision makers at a disease foundation want to store raw data generated a generic knowledge base driven from a corpus of PDF files stored locally within a specific laboratory
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Screenshots
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Screenshots