Transcript Rector presentation - School of Computer Science

Linking Formal Ontologies: Scale, Granularity
and Context
Alan Rector
Medical Informatics Group, University of Manchester
www.cs.man.ac.uk/mig
www.opengalen.org
img.cs.man.ac.uk
[email protected]
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Why use Logic-based Ontologies?
because
Knowledge is Fractal!
&
Changeable!
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Four Roles of Terminology/Ontologies
• Content of Databases and Patient Records
– Structural linkage within EPR/EHR & messages
– Content of EPR/EHR & messages
• Capturing information - the user interface
• Linkage between domains
–
–
–
–
–
Health and Bio Sciences
Macro, Micro, and Molecular scales
Contexts: Normal / abnormal; species; stage of development
Healthcare delivery and Clinical research
Patient Records and Decision Support
• Indexing Information
– Metadata and the semantic web
• www.semanticweb.org www.w3c.org
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Logic based ontologies
• The descendants of frame systems and object
hierarchies via KL-ONE
• “is-kind-of” = “implies”
– “Dog is a kind of wolf”
means
“All dogs are wolves”
– Therefore logically computable
• Modern examples: OIL, DAML+OIL (“OWL”?)
– Underpinned by the FaCT family of Description Logic Reasoners
• Others LOOM, CLASSIC, BACK, GRAIL,...
• www.ontoknowledge.org/oil www.semanticweb.org
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Logic-based Ontologies:
Conceptual Lego
gene
hand
protein
cell
extremity
expression
body
Lung
chronic
inflammation
acute
infection
bacterial
abnormal
deletion
normal
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polymorphism
ischaemic
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Logic-based Ontologies:
Conceptual Lego
“SNPolymorphism of CFTRGene causing Defect in MembraneTransport of ChlorideIon
causing Increase in Viscosity of Mucus in CysticFibrosis…”
“Hand which is
anatomically
normal”
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What’s in a “Logic based ontology”?
• Primitive concepts - in a hierarchy
– Described but not defined
• Properties - relations between concepts
– Also in a hierarchy
• Descriptors - property-concept pairs
– qualified by “some”, “only”, “at least”, “at most”
• Defined concepts
– Made from primitive concepts and descriptors
• Axioms
– disjointness, further description of defined concepts
• A Reasoner
– to organise it for you
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Logic Based Ontologies: A crash course
Thing
Feature
Structure
pathological
Heart
Thing
red
+ feature: pathological
MitralValve
Encrustation
* ALWAYS partOf: Heart
* ALWAYS feature: pathological
Structure
+ feature: pathological
+ involves: Heart
red
+ partOf: Heart
Encrustation
+ involves: MitralValve
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+ (feature: pathological)
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Bridging Bio and Health Informatics
• Define concepts with ‘pieces’ from different
scales and disciplines
– “Polymorphism which causes defect which causes disease”
• Define concepts which make context explicit
– “ ‘Hand which is anatomically normal’ 
has five fingers”
• Separate properties for different contexts/views
– “Abnormalities of clinical parts of the heart”
• includes pericardium
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Species
Bridging Scales
and context with
Ontologies
Genes
Protein
Function
Gene in Species
Disease
Protein coded by
gene in species
Function of
Protein coded by
gene in species
Disease caused by abnormality in
Function of
Protein coded by
gene in species
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Representing context and views by
variant properties
Organ
is_part_of
is_clinically_part_of
Pericardium
OrganPart
Heart
is_structurally_part_of
Disease of (is_part_of) Heart
CardiacValve
Disease of
Pericardium
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The cost: Ontologies are not Thesauri
A Mixed Hierarchy
organ
heart
heart valve
aortic valve
} kind
} part
} kind
} part
aortic valve cu sp
Works for navigation by humans
Works for “Disease of…’ and ‘Procedure on…’
Fails for “Surface of…”
How can the computer know the difference?
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From a thesaurus to a logic-based ontology
Untangle part-whole and is-kind-of in anatomic ontology
Link Clinical Ontology with Anatomical ontology
Add rule that “Disorder of part  disorder of whole”
Reasoner can then create automatically:
A logic-based is-kind-of (subsumption) hierarchy
disorder of organ
disorder of heart
disorder of valve in heart
disorder of aortic valve in heart
disorder of cusp in aortic valve in heart
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Examples common in Bio Ontologies
Golgi membrane
Integral protein
Plasma membrane
Apical plasma membrane
Is part of
Is part of
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The Cost: Normalising (untangling) Ontologies
Structure
Structure
Part-whole
Function
Function
Part-whole
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The Cost: Normalising (untangling) Ontologies
Making each meaning explicit and separate
PhysSubstance
Protein
‘ProteinHormone
ProteinHormone’
Insulin
Enzyme
‘Enzyme’
Steroid
SteroidHormone
‘SteroidHormone’
‘Hormone’
Hormone
ProteinHormone^
‘ProteinHormone’
Insulin^
SteroidHormone^
‘SteroidHormone’
Catalyst
‘Catalyst’
‘Enzyme’
Enzyme^
...and helping keep argument
rational and meetings
short!
…
ActionRole
PhysiologicRole
HormoneRole
CatalystRole
…
Hormone
ProteinHormone
SteroidHormone
Catalyst
Enzyme
=
=
=
=
…
Substance
BodySubstance
Protein
Steroid
…
Substance & playsRole-HormoneRole
Protein & playsRole-HormoneRole
Steroid & playsRole-HormoneRole
Substance & playsRole CatalystRole
?=? Protein & playsRole-CatalystRole
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The Cost
• You can’t say everything you want to
– Expressiveness costs computational complexity
• More inference takes more time
– Scaling for complex tasks still being investigated
• Many other kinds of reasoning needed
It doesn’t make the! Coffee!
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Other benefits
• Limit combinatorial explosions
From “phrase book” to “dictionary + grammar”
Avoid the “exploding bicycle”
–
–
–
–
1980 - ICD-9 (E826) 8
1990 - READ-2 (T30..) 81
1995 - READ-3 87
1996 - ICD-10 (V10-19) 587
• V31.22 Occupant of three-wheeled motor vehicle injured in collision with pedal cycle,
person on outside of vehicle, nontraffic accident, while working for income
– and meanwhile elsewhere in ICD-10
• W65.40 Drowning and submersion while in bath-tub, street and highway, while engaged
in sports activity
• X35.44 Victim of volcanic eruption, street and highway, while resting, sleeping, eating or
engaging in other vital activities
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Other benefits
• Index and assemble information
Hypertension
Idiopathic
IdiopathicHypertension
Hypertension`
Inour
ourcompany’s
company’sstudies
studies
In
Study a
Study a phase 2
Phase
Phase22
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Summary:
Logic based ontologies because
Knowledge is Fractal
• Link “Conceptual Lego”
– at all levels
• indefinitely
– Spanning scales, genotype, phenotype, etc.
• Model context and views
– Express differences explicitly
• Manage combinatorial explosion
• Index information efficiently
Next step:
Larger scale demonstrations in Genotype to Phenotype
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