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 www.cs.man.ac.uk/mig www.opengalen.org img.cs.man.ac.uk mygrid.man.ac.uk rector@cs.man.ac.uk

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

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

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

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,... www.ontoknowledge.org/oil www.semanticweb.org http://oiled.man.ac.uk

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

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”

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

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)

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

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

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

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

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?

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

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

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

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

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

Integrating rather than Cross Mapping

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

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)

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

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

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

…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))>))))

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

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

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

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!

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

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