Linking levels of granularity and expressing contexts

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Linking levels of granularity and expressing
contexts views using formal ontologies
Experience with the Digital Anatomist FMA
other health bio ontologies

• Alan Rector
• Medical Informatics Group, University of
  Manchesterwww.cs.man.ac.uk/migwww.opengalen.org
  img.cs.man.ac.ukmygrid.man.ac.ukrector_at_cs.man.a
  c.uk

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Messages

• Logic-based ontologies work to manage
  granularity, scale, and context
• If you normalise (untangle) them
• And you make special provision for part-whole
  relations
• Developing formal ontologies is best done through
  Intermediate Representations
• Formal ontologies are the Assembly languages of
  knowledge representation
• Use specialised high level languages for
  applications but define their semantics well
• Preserve work despite changes in underlying
  technology
• Focus on process not product
• Just in time ontologies
• Language (terms) must be separated from
  concepts
• Conflation ? false problems
• Synonymy / homonymy etc. are linguistic issues

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Why use Logic-based Ontologies?
becauseKnowledge is Fractal!
Changeable!
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Five Roles for Terminology/Ontologies

• Software Engineering
• Saying each thing in exactly one place
• In a way usable by computers
• Coherence without uniformity
• Evolvable
• 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
• Content of Databases and Patient Records
• Structural linkage within EPR/EHR messages
• Content of EPR/EHR messages
• Capturing information - the user interface

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Logic based ontologies

• The descendants / partial formalisation of
  semantic nets, frame systems, and object
  hierarchies via KL-ONE and KRL
• is-kind-of implies
• Dog is a kind of wolf meansAll dogs are
  wolves
• Therefore logically computable
• Modern examples OIL, DAMLOIL (OWL?)
• OKBC meets Logic Based Ontologies
• Underpinned by Description Logic Reasoners (FaCT,
  RACER, Cerebra- older GRAIL, K-REP)
• Others LOOM, CLASSIC, BACK, GRAIL,...
• www.ontoknowledge.org/oil www.semanticweb.org
• http//oiled.man.ac.uk

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Logic-based Ontologies Conceptual Lego
gene
protein
cell
expression
chronic
acute
bacterial
deletion
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 isanatomicallynormal
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Whats 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
Primitives
Descriptions
Definitions
Reasoning
Validating
Thing
red partOf Heart
red partOf Heart
(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

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Bridging Scales and context with Ontologies
Species
Genes
Function
Disease
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Using composition to express context

• Normal and abnormal
• Hand ? isSubdivisionOf some UpperExtremity
• Hand AnatomicallyNormal ? hasSubdivision
  exactly-5 fingers
• Homologies and Orthologies
• Thumb of Hand of Human ?
  hasFeature Opposable
• Thumb of Hand of NonHumanPrimate ?
  not hasFeature
  Opposable

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Representing context and views by variant
properties
is_part_of
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The cost Ontologies are not Thesauri
A Mixed Hierarchy (Not from Digital Anatomist)
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
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The Cost Normalising (untangling) Ontologies
The Digital Anatomist FMA is very well untangled
therefore a good starting point
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The Cost Normalising (untangling)
OntologiesMaking each meaning explicit and
separate
PhysSubstance Protein ProteinHormone
Insulin Enzyme Steroid
SteroidHormone Hormone ProteinHormone
Insulin SteroidHormone
Catalyst Enzyme
PhysSubstance Protein ProteinHormone
Insulin Enzyme Steroid
SteroidHormone Hormone
ProteinHormone Insulin
SteroidHormone Catalyst Enzyme
...and helping keep argument rational and
meetings short!
Hormone Substance playsRole-HormoneRole Pro
teinHormone Protein playsRole-HormoneRoleS
teroidHormone Steroid playsRole-HormoneRole
Catalyst Substance playsRole CatalystRole
Enzyme ?? Protein playsRole-CatalystRole
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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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Linking ontologies to integrate or to Map
Mapping Solution
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Integrating rather than Cross Mapping
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Special Requirements for AnatomyParts and Wholes

• Disorders of the part are disorders of the whole,
  
• e.g. Diseases of the aortic valve are
  diseases of the heart
• One solutions description logic solution rewrite
  
• Disease of Heart ? Disease of (Heart OR Part
  of Heart)
• SEP triples Shulz and Hahn, U Freiburg
• Subdivisions of layers are layers of subdivisions
• e.g. The skin of the hand is a subdivision of
  the skin of the upper extremity
• No scalable DL solution known
• Needs additional rules (Rousset) or extensive
  rewriting
• Intermediate representation essential

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Fractal vs Bridgin principles

• Fractal
• Substances make up Structues
• Aggregations of things at one scale make up
  substances/structures at the next
• E.g. Cells ? Tissues molecules ? substances
• Processes act on Things or other processes
• Local
• Atoms are bound in molecules
• Genes code for proteins (in many ways and
  variations)

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Adding a High Level LanguageIntermediate
Representations

• Domain experts should work in a domain oriented
  language
• A View for each application
• Enforcing standards
• Buffering differences
• Capture all of the information in one place
  including
• Metadata for provenance, editorial status, etc.
• Comments for users
• Comments for developers
• Links to external knowledge sources

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An Example from OpenGALENSimplicity for
terminologists...

• "Open fixation of a fracture of the neck of the
  left femur"
• MAIN fixing
• ACTS_ON fracture
• HAS_LOCATION neck of long bone
• IS_PART_OF femur
• HAS_LATERALITY left
• HAS_APPROACH open

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Simplicity for End Users...
FRACTURE SURGERY
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but with a solid, formal foundation(that no one
wants to see or work in)
(SurgicalProcess which isMainlyCharacterisedBy
(performance which isEnactmentOf
(SurgicalFixing which

• hasSpecificSubprocess (SurgicalAccessing
• hasSurgicalOpenClosedness
  (SurgicalOpenClosedness which
• hasAbsoluteState surgicallyOpen))

actsSpecificallyOn (PathologicalBodyStruc
ture which lt involves Bone
hasUniqueAssociatedProcess
FracturingProcess hasSpecificLocat
ion (Collum which
isSpecificSolidDivisionOf
(Femur which
hasLeftRightSelector
leftSelection))gt))))
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Experience of Intermediate Representation

• Training time
• Time to do independent work 3 months ? 3 days
  3 days
• Productivity
• Raw 50/day ? 150/day
• Review 50 of effort ? 10 of effort
• Arguments Many per cycle ? rare
• Evolution updates for revision or schemas
  Months ? weeks or days
• Coupling
• Dependencies High ? Low
• Focus
• Technical and clinical/scientific conflated ?
  clearly separated

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Language and Concepts

• Concepts units of thought
• e.g. Tumour which is malignant Tumour
  whether benign or malignant
• Can be similar or logically equivalent
• Operations are logical
• Terms units of language
• e.g. Neoplasm Malignant tumour
  Cancer
• Can be synonyms, homonyms, metonyms, etc.
• Operations are lexical and linguistic
• Many arguments involve confusing the two
• Rectors Law The length of argument inversely
  proportional to strength of evidence

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It works for what it does

• Faster, more accurate development updating
• Dutch experience Cost cut by 70
• Mostly by reducing unproductive arguments
  committee meetings
• The French experience
• A practical way to agree
• Gene Ontology
• About 10 additional subsumptions in initial
  tests
• myGrid
• Web service description/sepcification
• Reduces granularity of consensus required
• Coherence without uniformity
• Experience in
• GALEN
• Successful loosely coupled collaborations
• The Drug Ontology
• Untangled forms and routes in six weeks after 2
  years prior frustration with simple methods

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But it has limitationsA specialised fragment of
logic for a specialised task

• Not enough on its own needs other tools for
• Defaults exceptions, meta models, constraint
  based reasoning, full first order logic for
  Decision Support, quntities units, complex
  spatio-temporal reasoning
• Best used as part of a larger environment
• As complement to frame system, e.g. Protégé?
• Limitations even within the paradigm
• Expressiveness costs computational complexity
• Scaling for complex tasks still being
  investigated
• Use in very large ontologies still developing
• Interactions of features a highly specialised
  topic

It doesnt make the! Coffee!
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Relevant Experience with Logic Based Ontologies
in Bio Health Applications

• Health informatics
• Surgical procedures and diseases
• Development of French National Classification
  maintenance of Dutch
• UK Drug ontology HL7 forms and routes analysis
• Gene Ontology Next Generation (GONG)
• CLEF integration and language engineering in
  Cancer Research
• Analysis of UMLS (Shulz Hahn, Freiburg)
• MedSyndicate Language engineering in
  biomedicine (Schulz Hahn, Freiburg)
• SNOMED RT/CT (CAP/Apelon)
• Reprepresentation of Digital Anatomist FMA(Early
  experiments only)
• Bioinformatics
• Tambis information fusion and database
  mediation
• myGrid web services for bioinformatics
• IRBAIN Language engineering in bioinfomatics
• Starch Art history
• Multiflora Language Engineering and Cladistics
  in Botany

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Summary Logic based ontologies

• Work to manage granularity, scale, and context
• If you normalise (untangle) them
• And you make special provision for part-whole
  relations
• Are best developed through Intermediate
  Representations
• Formal ontologies are the Assembly languages of
  knowledge representation
• Use specialised high level languages for
  applications but define their semantics well
• Focus on Process rather than product
• Just in time ontologies
• Save time and make work more reliable
• Require separation of language (terms) from
  concepts
• Conflation ? false problems
• Have limitations
• Need to be embedded in a broader environment
• Hybrid representation systems