1. 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 Manchester img.cs.man.ac.uk mygrid.man.ac.uk

2. 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

3. Why use Logic-based Ontologies?
because Knowledge is Fractal!
& Changeable!

4. 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
Content of Databases and Patient Records
Structural linkage within EPR/EHR & messages
Content of EPR/EHR & messages
Capturing information - the user interface

5. 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” means “All dogs are wolves”
Therefore logically computable
Modern examples: OIL, DAML+OIL (“OWL”?)
“OKBC meets Logic Based Ontologies”
Underpinned by Description Logic Reasoners (FaCT, RACER, Cerebra- older GRAIL, K-REP)
Others LOOM, CLASSIC, BACK, GRAIL,...

6. Logic-based Ontologies: Conceptual Lego
gene
hand
extremity
body
protein
cell
expression
infection
inflammation
Lung
chronic
acute
bacterial
abnormal
normal
deletion
polymorphism
ischaemic

7. 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”

8. 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

9. Logic Based Ontologies: A crash course
Primitives
Descriptions
Definitions
Reasoning
Validating
Thing
Feature
pathological
red
Structure
Encrustation
+ involves: MitralValve
Thing
+ feature: pathological
Structure
+ involves: Heart
Heart
MitralValve
Encrustation
MitralValve
* ALWAYS partOf: Heart
Encrustation
* ALWAYS feature: pathological
red
+ partOf: Heart
red
+ partOf: Heart
+ (feature: pathological)

10. Bridging Bio and Health Informatics
Define concepts with ‘pieces’ from different scales and disciplines
“Polymorphism which causes defect which causes disease”

11. Bridging Scales and context with Ontologies
Species
Genes
Gene in Species
Protein
Protein coded by gene in species
Function
Function of Protein coded by gene in species
Disease
Disease caused by abnormality in Function of Protein coded by gene in species

12. 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

13. Representing context and views by variant properties
is_part_of
Disease of (is_part_of) Heart
Disease of Pericardium
Organ
Heart
Pericardium
OrganPart
CardiacValve
is_clinically_part_of
is_structurally_part_of

14. 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?

15. 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

16. The Cost: Normalising (untangling) Ontologies
Structure
Function
Part-whole
Structure
Function
Part-whole
The Digital Anatomist FMA is very well untangled – therefore a good starting point

17. The Cost: Normalising (untangling) Ontologies Making 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
ProteinHormone = Protein & playsRole-HormoneRole SteroidHormone = Steroid & playsRole-HormoneRole
Catalyst = Substance & playsRole CatalystRole
Enzyme ?=? Protein & playsRole-CatalystRole

18. Other benefits
Limit combinatorial explosions From “phrase book” to “dictionary + grammar” Avoid the “exploding bicycle”
ICD-9 (E826) 8
READ-2 (T30..) 81
READ-3 87
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

19. Linking ontologies: to integrate or to Map: Mapping Solution
Hypertension
Hypertension which occurs-in pregnancy
...
Diseases of Pregnancy
Hypertension in Pregnancy
Hypertension (excluding Pregnancy)
CV Diseases

20. Integrating rather than Cross Mapping

21. Special Requirements for Anatomy Parts 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

22. Fractal vs Bridgin principles
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)

23. Adding a High Level Language Intermediate 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

24. An Example from OpenGALEN Simplicity 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

25. Simplicity for End Users...
Structured Data Entry
File Edit Help
FRACTURE SURGERY
Reduction
Fixation
Fixation
Open
Closed
Open
Tibia
Fibula
Ankle
More...
Radius
Ulna
Wrist
Humerus
Femur
Femur
Left
Left
Right
More...
Gt Troch
Shaft
Neck
Neck

26. …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 (PathologicalBodyStructure which <
involves Bone
hasUniqueAssociatedProcess FracturingProcess
hasSpecificLocation (Collum which
isSpecificSolidDivisionOf (Femur which
hasLeftRightSelector leftSelection))>))))

27. 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

28. 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
Rector’s Law: The length of argument inversely proportional to strength of evidence

29. 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

30. It doesn’t make the! Coffee!
But it has limitations: A 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 doesn’t make the! Coffee!

31. 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

32. 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